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Qiang Yang

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159 papers
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159

AAAI Conference 2026 Conference Paper

Federated Vision-Language-Recommendation with Personalized Fusion

  • Zhiwei Li
  • Guodong Long
  • Jing Jiang
  • Chengqi Zhang
  • Qiang Yang

Applying large pre-trained Vision-Language Models to recommendation is a burgeoning field, a direction we term Vision-Language-Recommendation (VLR). Bringing VLR to user-oriented on-device intelligence within a federated learning framework is a crucial step for enhancing user privacy and delivering personalized experiences. This paper introduces FedVLR, a federated VLR framework specially designed for user-specific personalized fusion of vision-language representations. At its core is a novel bi-level fusion mechanism: The server-side multi-view fusion module first generates a diverse set of pre-fused multimodal views. Subsequently, each client employs a user-specific mixture-of-expert mechanism to adaptively integrate these views based on individual user interaction history. This designed lightweight personalized fusion module provides an efficient solution to implement a federated VLR system. The effectiveness of our proposed FedVLR has been validated on seven benchmark datasets.

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AAAI Conference 2026 Conference Paper

FedGRPO: Privately Optimizing Foundation Models with Group-Relative Rewards from Domain Clients

  • Gongxi Zhu
  • Hanlin Gu
  • Lixin Fan
  • Qiang Yang
  • Yuxing Han

One important direction of Federated Foundation Models (FedFMs) is leveraging data from small client models to enhance the performance of a large server‑side foundation model. Existing methods based on model level or representation level knowledge transfer either require expensive local training or incur high communication costs and introduce unavoidable privacy risks. We reformulate this problem as a reinforcement learning style evaluation process and propose FedGRPO, a privacy preserving framework comprising two modules. The first module performs competence-based expert selection by building a lightweight confidence graph from auxiliary data to identify the most suitable clients for each question. The second module leverages the “Group Relative” concept from the Group Relative Policy Optimization (GRPO) framework by packaging each question together with its solution rationale into candidate policies, dispatching these policies to a selected subset of expert clients, and aggregating solely the resulting scalar reward signals via a federated group–relative loss function. By exchanging reward values instead of data or model updates, FedGRPO reduces privacy risk and communication overhead while enabling parallel evaluation across heterogeneous devices. Empirical results on diverse domain tasks demonstrate that FedGRPO achieves superior downstream accuracy and communication efficiency compared to conventional FedFMs baselines.

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TIST Journal 2026 Journal Article

FedPRS: A Privacy-preserving Representation Synthesis Framework for Federated Contribution Evaluation

  • Yuwei Fan
  • Yuan Yao
  • Wei Xi
  • Quan Zhao
  • Zelei Liu
  • Lixin Fan
  • Qiang Yang
  • Jian Jin

Federated Learning (FL) enables the collaborative training of a global model while protecting participants’ privacy. Evaluating each participant’s contribution is essential to providing a high-quality model, ensuring fairness, and mitigating potential biases. Most existing contribution evaluation approaches for FL assume that the server has a public validation dataset. However, it is almost impossible to obtain a validation dataset due to privacy concerns. In this article, we propose a Federated Privacy-preserving Representation Synthesis (FedPRS) framework to synthesize a validation dataset for contribution evaluation. The proposed FedPRS framework first transforms each participant’s private validation dataset into its representation. Then, a random-region desensitization strategy is developed to further desensitize the dataset without compromising its utility. The desensitized representation dataset of each participant is collected by the server to evaluate federated contribution, which considers both equity and privacy protection. Moreover, we instantiate and integrate three specific contribution evaluation approaches in this framework. We perform experiments on various FL settings, including independently identically distributed (IID) and non-IID data distributions. Experimental results demonstrate that the contribution evaluation results obtained using the validation dataset synthesized by the FedPRS framework are closely aligned with those obtained using a real, private validation dataset.

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AAAI Conference 2026 Conference Paper

Oblivionis: A Lightweight Learning and Unlearning Framework for Federated Large Language Models

  • Fuyao Zhang
  • Xinyu Yan
  • Tiantong Wu
  • Wenjie Li
  • Tianxiang Chen
  • Yang Cao
  • Ran Yan
  • Longtao Huang

Large Language Models (LLMs) increasingly leverage Federated Learning (FL) to utilize private, task-specific datasets for fine-tuning while preserving data privacy. However, while federated LLM frameworks effectively enable collaborative training without raw data sharing, they critically lack built-in mechanisms for regulatory compliance like GDPR’s right to be forgotten. Integrating private data heightens concerns over data quality and long-term governance, yet existing distributed training frameworks offer no principled way to selectively remove specific client contributions post-training. Due to distributed data silos, stringent privacy constraints, and the intricacies of interdependent model aggregation, federated LLM unlearning is significantly more complex than centralized LLM unlearning. To address this gap, we introduce Oblivionis, a lightweight learning and unlearning framework that enables clients to selectively remove specific private data during federated LLM training, enhancing trustworthiness and regulatory compliance. By unifying FL and unlearning as a dual optimization objective, we incorporate 6 FL and 5 unlearning algorithms for comprehensive evaluation and comparative analysis, establishing a robust pipeline for federated LLM unlearning. Extensive experiments demonstrate that Oblivionis outperforms local training, achieving a robust balance between forgetting efficacy and model utility, with cross-algorithm comparisons providing clear directions for future LLM development.

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TIST Journal 2025 Journal Article

Cross-User Federated Recommendation Unlearning

  • Yang Li
  • Enyue Yang
  • Weike Pan
  • Qiang Yang
  • Zhong Ming

Cross-user federated recommendation (CUFR) is a promising solution for providing personalized services without collecting users’ raw data. However, most previous CUFR works mainly focus on providing accurate and privacy-preserving personalized recommendations, but overlook the fact that users can opt out at any time during the training process. In response, we study an emerging and new problem of efficiently training an unlearned model to forget the data of the clients who leave a federated system. It is challenging to simply apply or slightly modify existing machine unlearning or federated unlearning methods to CUFR because of the unique collaboration effect in recommender systems. Although a recent gradient calibration-based method (i.e., FRU) shows promising in training an unlearned model, there are still some limitations: (i) there is a potential possibility that some clients run out of the storage space, (ii) all the remaining clients need to participate in computing the new gradients, (iii) it masks the uniqueness of the local gradients, and (iv) the errors of the calibrated gradients will increase gradually with more iterations. In this article, we propose a novel CUFR unlearning (CUFRU) method. Specifically, we design a gradient transfer station (GTS) module for storing the historical gradients while enabling clients to dynamically participate in the computation of the calibrated gradients with the new gradients based on their online status. Moreover, we design a novel iteration-aware gradient calibration mechanism to strike a balance between the weights of the historical and new gradients at the different stages of the unlearning process, alleviating the calibration errors. Finally, we conduct extensive experiments on three real-world datasets to show that our CUFRU can more efficiently train an unlearned model with the competitive recommendation performance.

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TIST Journal 2025 Journal Article

Grounding Foundation Models through Federated Transfer Learning: A General Framework

  • Yan Kang
  • Tao Fan
  • Hanlin Gu
  • Xiaojin Zhang
  • Lixin Fan
  • Qiang Yang

Foundation Models (FMs) such as GPT-4 encoded with vast knowledge and powerful emergent abilities have achieved remarkable success in various natural language processing and computer vision tasks. Grounding FMs by adapting them to domain-specific tasks or augmenting them with domain-specific knowledge enables us to exploit the full potential of FMs. However, grounding FMs faces several challenges, stemming primarily from constrained computing resources, data privacy, model heterogeneity, and model ownership. Federated Transfer Learning (FTL), the combination of federated learning and transfer learning, provides promising solutions to address these challenges. Recently, the need for grounding FMs leveraging FTL, coined FTL-FM, has arisen strongly in both academia and industry. Motivated by the strong growth in FTL-FM research and the potential impact of FTL-FM on industrial applications, we propose an FTL-FM framework that formulates problems of grounding FMs in the federated learning setting, construct a detailed taxonomy based on the FTL-FM framework to categorize state-of-the-art FTL-FM works, and comprehensively overview FTL-FM works based on the proposed taxonomy. We also establish correspondence between FTL-FM and conventional phases of adapting FM so that FM practitioners can align their research works with FTL-FM. In addition, we overview advanced efficiency-improving and privacy-preserving techniques because efficiency and privacy are critical concerns in FTL-FM. Last, we discuss opportunities and future research directions of FTL-FM.

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TIST Journal 2024 Journal Article

A Game-theoretic Framework for Privacy-preserving Federated Learning

  • Xiaojin Zhang
  • Lixin Fan
  • Siwei Wang
  • Wenjie Li
  • Kai Chen
  • Qiang Yang

In federated learning, benign participants aim to optimize a global model collaboratively. However, the risk of privacy leakage cannot be ignored in the presence of semi-honest adversaries. Existing research has focused either on designing protection mechanisms or on inventing attacking mechanisms. While the battle between defenders and attackers seems never-ending, we are concerned with one critical question: Is it possible to prevent potential attacks in advance? To address this, we propose the first game-theoretic framework that considers both FL defenders and attackers in terms of their respective payoffs, which include computational costs, FL model utilities, and privacy leakage risks. We name this game the federated learning privacy game (FLPG), in which neither defenders nor attackers are aware of all participants’ payoffs. To handle the incomplete information inherent in this situation, we propose associating the FLPG with an oracle that has two primary responsibilities. First, the oracle provides lower and upper bounds of the payoffs for the players. Second, the oracle acts as a correlation device, privately providing suggested actions to each player. With this novel framework, we analyze the optimal strategies of defenders and attackers. Furthermore, we derive and demonstrate conditions under which the attacker, as a rational decision-maker, should always follow the oracle’s suggestion not to attack.

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TIST Journal 2024 Journal Article

A Meta-Learning Framework for Tuning Parameters of Protection Mechanisms in Trustworthy Federated Learning

  • Xiaojin Zhang
  • Yan Kang
  • Lixin Fan
  • Kai Chen
  • Qiang Yang

Trustworthy federated learning typically leverages protection mechanisms to guarantee privacy. However, protection mechanisms inevitably introduce utility loss or efficiency reduction while protecting data privacy. Therefore, protection mechanisms and their parameters should be carefully chosen to strike an optimal tradeoff among privacy leakage, utility loss, and efficiency reduction. To this end, federated learning practitioners need tools to measure the three factors and optimize the tradeoff between them to choose the protection mechanism that is most appropriate to the application at hand. Motivated by this requirement, we propose a framework that (1) formulates trustworthy federated learning as a problem of finding a protection mechanism to optimize the tradeoff among privacy leakage, utility loss, and efficiency reduction and (2) formally defines bounded measurements of the three factors. We then propose a meta-learning algorithm to approximate this optimization problem and find optimal protection parameters for representative protection mechanisms, including randomization, homomorphic encryption, secret sharing, and compression. We further design estimation algorithms to quantify these found optimal protection parameters in a practical horizontal federated learning setting and provide a theoretical analysis of the estimation error.

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IJCAI Conference 2024 Conference Paper

A Survey on Cross-Domain Sequential Recommendation

  • shu chen
  • Zitao Xu
  • Weike Pan
  • Qiang Yang
  • Zhong Ming

Cross-domain sequential recommendation (CDSR) shifts the modeling of user preferences from flat to stereoscopic by integrating and learning interaction information from multiple domains at different granularities (ranging from inter-sequence to intra-sequence and from single-domain to cross-domain). In this survey, we initially define the CDSR problem using a four-dimensional tensor and then analyze its multi-type input representations under multidirectional dimensionality reductions. Following that, we provide a systematic overview from both macro and micro views. From a macro view, we abstract the multi-level fusion structures of various models across domains and discuss their bridges for fusion. From a micro view, focusing on the existing models, we specifically discuss the basic technologies and then explain the auxiliary learning technologies. Finally, we exhibit the available public datasets and the representative experimental results as well as provide some insights into future directions for research in CDSR.

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TIST Journal 2024 Journal Article

A Survey on Evaluation of Large Language Models

  • Yupeng Chang
  • Xu Wang
  • Jindong Wang
  • Yuan Wu
  • Linyi Yang
  • Kaijie Zhu
  • Hao Chen
  • Xiaoyuan Yi

Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide an overview from the perspective of evaluation tasks, encompassing general natural language processing tasks, reasoning, medical usage, ethics, education, natural and social sciences, agent applications, and other areas. Secondly, we answer the ‘where’ and ‘how’ questions by diving into the evaluation methods and benchmarks, which serve as crucial components in assessing the performance of LLMs. Then, we summarize the success and failure cases of LLMs in different tasks. Finally, we shed light on several future challenges that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to researchers in the realm of LLMs evaluation, thereby aiding the development of more proficient LLMs. Our key point is that evaluation should be treated as an essential discipline to better assist the development of LLMs. We consistently maintain the related open-source materials at: https://github.com/MLGroupJLU/LLM-eval-survey

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AAAI Conference 2024 Conference Paper

Complementary Knowledge Distillation for Robust and Privacy-Preserving Model Serving in Vertical Federated Learning

  • Dashan Gao
  • Sheng Wan
  • Lixin Fan
  • Xin Yao
  • Qiang Yang

Vertical Federated Learning (VFL) enables an active party with labeled data to enhance model performance (utility) by collaborating with multiple passive parties that possess auxiliary features corresponding to the same sample identifiers (IDs). Model serving in VFL is vital for real-world, delay-sensitive applications, and it faces two major challenges: 1) robustness against arbitrarily-aligned data and stragglers; and 2) privacy protection, ensuring minimal label leakage to passive parties. Existing methods fail to transfer knowledge among parties to improve robustness in a privacy-preserving way. In this paper, we introduce a privacy-preserving knowledge transfer framework, Complementary Knowledge Distillation (CKD), designed to enhance the robustness and privacy of multi-party VFL systems. Specifically, we formulate a Complementary Label Coding (CLC) objective to encode only complementary label information of the active party's local model for passive parties to learn. Then, CKD selectively transfers the CLC-encoded complementary knowledge 1) from the passive parties to the active party, and 2) among the passive parties themselves. Experimental results on four real-world datasets demonstrate that CKD outperforms existing approaches in terms of robustness against arbitrarily-aligned data, while also minimizing label privacy leakage.

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TIST Journal 2024 Journal Article

Decentralized Federated Recommendation with Privacy-aware Structured Client-level Graph

  • Zhitao Li
  • Zhaohao Lin
  • Feng Liang
  • Weike Pan
  • Qiang Yang
  • Zhong Ming

Recommendation models are deployed in a variety of commercial applications to provide personalized services for users. However, most of them rely on the users’ original rating records that are often collected by a centralized server for model training, which may cause privacy issues. Recently, some centralized federated recommendation models are proposed for the protection of users’ privacy, which however requires a server for coordination in the whole process of model training. As a response, we propose a novel privacy-aware decentralized federated recommendation (DFedRec) model, which is lossless compared with the traditional model in recommendation performance and is thus more accurate than other models in this line. Specifically, we design a privacy-aware structured client-level graph for the sharing of the model parameters in the process of model training, which is a one-stone-two-bird strategy, i.e., it protects users’ privacy via some randomly sampled fake entries and reduces the communication cost by sharing the model parameters only with the related neighboring users. With the help of the privacy-aware structured client-level graph, we propose two novel collaborative training mechanisms in the setting without a server, including a batch algorithm DFedRec(b) and a stochastic one DFedRec(s), where the former requires the anonymity mechanism while the latter does not. They are both equivalent to probabilistic matrix factorization trained in a centralized server and are thus lossless. We then provide formal analysis of privacy guarantee of our methods and conduct extensive empirical studies on three public datasets with explicit feedback, which show the effectiveness of our DFedRec, i.e., it is privacy aware, communication efficient, and lossless.

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TIST Journal 2024 Journal Article

Optimizing Privacy, Utility, and Efficiency in a Constrained Multi-Objective Federated Learning Framework

  • Yan Kang
  • Hanlin Gu
  • Xingxing Tang
  • Yuanqin He
  • Yuzhu Zhang
  • Jinnan He
  • Yuxing Han
  • Lixin Fan

Conventionally, federated learning aims to optimize a single objective, typically the utility. However, for a federated learning system to be trustworthy, it needs to simultaneously satisfy multiple objectives, such as maximizing model performance, minimizing privacy leakage and training costs, and being robust to malicious attacks. Multi-Objective Optimization (MOO) aiming to optimize multiple conflicting objectives simultaneously is quite suitable for solving the optimization problem of Trustworthy Federated Learning (TFL). In this article, we unify MOO and TFL by formulating the problem of constrained multi-objective federated learning (CMOFL). Under this formulation, existing MOO algorithms can be adapted to TFL straightforwardly. Different from existing CMOFL algorithms focusing on utility, efficiency, fairness, and robustness, we consider optimizing privacy leakage along with utility loss and training cost, the three primary objectives of a TFL system. We develop two improved CMOFL algorithms based on NSGA-II and PSL, respectively, to effectively and efficiently find Pareto optimal solutions and provide theoretical analysis on their convergence. We design quantitative measurements of privacy leakage, utility loss, and training cost for three privacy protection mechanisms: Randomization, BatchCrypt (an efficient homomorphic encryption), and Sparsification. Empirical experiments conducted under the three protection mechanisms demonstrate the effectiveness of our proposed algorithms.

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NeurIPS Conference 2024 Conference Paper

Shopping MMLU: A Massive Multi-Task Online Shopping Benchmark for Large Language Models

  • Yilun Jin
  • Zheng Li
  • Chenwei Zhang
  • Tianyu Cao
  • Yifan Gao
  • Pratik Jayarao
  • Mao Li
  • Xin Liu

Online shopping is a complex multi-task, few-shot learning problem with a wide and evolving range of entities, relations, and tasks. However, existing models and benchmarks are commonly tailored to specific tasks, falling short of capturing the full complexity of online shopping. Large Language Models (LLMs), with their multi-task and few-shot learning abilities, have the potential to profoundly transform online shopping by alleviating task-specific engineering efforts and by providing users with interactive conversations. Despite the potential, LLMs face unique challenges in online shopping, such as domain-specific concepts, implicit knowledge, and heterogeneous user behaviors. Motivated by the potential and challenges, we propose Shopping MMLU, a diverse multi-task online shopping benchmark derived from real-world Amazon data. Shopping MMLU consists of 57 tasks covering 4 major shopping skills: concept understanding, knowledge reasoning, user behavior alignment, and multi-linguality, and can thus comprehensively evaluate the abilities of LLMs as general shop assistants. With Shoppping MMLU, we benchmark over 20 existing LLMs and uncover valuable insights about practices and prospects of building versatile LLM-based shop assistants. Shopping MMLU can be publicly accessed at https: //github. com/KL4805/ShoppingMMLU. In addition, with Shopping MMLU, we are hosting a competition in KDD Cup 2024 with over 500 participating teams. The winning solutions and the associated workshop can be accessed at our website https: //amazon-kddcup24. github. io/.

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IJCAI Conference 2024 Conference Paper

Unlearning during Learning: An Efficient Federated Machine Unlearning Method

  • Hanlin Gu
  • Gongxi Zhu
  • Jie Zhang
  • Xinyuan Zhao
  • Yuxing Han
  • Lixin Fan
  • Qiang Yang

In recent years, Federated Learning (FL) has garnered significant attention as a distributed machine learning paradigm. To facilitate the implementation of the "right to be forgotten, " the concept of federated machine unlearning (FMU) has also emerged. However, current FMU approaches often involve additional time-consuming steps and may not offer comprehensive unlearning capabilities, which renders them less practical in real FL scenarios. In this paper, we introduce FedAU, an innovative and efficient FMU framework aimed at overcoming these limitations. Specifically, FedAU incorporates a lightweight auxiliary unlearning module into the learning process and employs a straightforward linear operation to facilitate unlearning. This approach eliminates the requirement for extra time-consuming steps, rendering it well-suited for FL. Furthermore, FedAU exhibits remarkable versatility. It not only enables multiple clients to carry out unlearning tasks concurrently but also supports unlearning at various levels of granularity, including individual data samples, specific classes, and even at the client level. We conducted extensive experiments on MNIST, CIFAR10, and CIFAR100 datasets to evaluate the performance of FedAU. The results demonstrate that FedAU effectively achieves the desired unlearning effect while maintaining model accuracy.

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AAAI Conference 2023 Conference Paper

Cross-Domain Few-Shot Graph Classification with a Reinforced Task Coordinator

  • Qiannan Zhang
  • Shichao Pei
  • Qiang Yang
  • Chuxu Zhang
  • Nitesh V. Chawla
  • Xiangliang Zhang

Cross-domain graph few-shot learning attempts to address the prevalent data scarcity issue in graph mining problems. However, the utilization of cross-domain data induces another intractable domain shift issue which severely degrades the generalization ability of cross-domain graph few-shot learning models. The combat with the domain shift issue is hindered due to the coarse utilization of source domains and the ignorance of accessible prompts. To address these challenges, in this paper, we design a novel Cross-domain Task Coordinator to leverage a small set of labeled target domain data as prompt tasks, then model the association and discover the relevance between meta-tasks from the source domain and the prompt tasks. Based on the discovered relevance, our model achieves adaptive task selection and enables the optimization of a graph learner using the selected fine-grained meta-tasks. Extensive experiments conducted on molecular property prediction benchmarks validate the effectiveness of our proposed method by comparing it with state-of-the-art baselines.

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IJCAI Conference 2023 Conference Paper

FedPass: Privacy-Preserving Vertical Federated Deep Learning with Adaptive Obfuscation

  • Hanlin Gu
  • Jiahuan Luo
  • Yan Kang
  • Lixin Fan
  • Qiang Yang

Vertical federated learning (VFL) allows an active party with labeled data to leverage auxiliary features from the passive parties to improve model performance. Concerns about the private feature and label leakage in both the training and inference phases of VFL have drawn wide research attention. In this paper, we propose a general privacy-preserving vertical federated deep learning framework called FedPass, which leverages adaptive obfuscation to protect the feature and label simultaneously. Strong privacy-preserving capabilities about private features and labels are theoretically proved (in Theorems 1 and 2). Extensive experimental results with different datasets and network architectures also justify the superiority of FedPass against existing methods in light of its near-optimal trade-off between privacy and model performance.

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TIST Journal 2023 Journal Article

Trading Off Privacy, Utility, and Efficiency in Federated Learning

  • Xiaojin Zhang
  • Yan Kang
  • Kai Chen
  • Lixin Fan
  • Qiang Yang

Federated learning (FL) enables participating parties to collaboratively build a global model with boosted utility without disclosing private data information. Appropriate protection mechanisms have to be adopted to fulfill the opposing requirements in preserving privacy and maintaining high model utility. In addition, it is a mandate for a federated learning system to achieve high efficiency in order to enable large-scale model training and deployment. We propose a unified federated learning framework that reconciles horizontal and vertical federated learning. Based on this framework, we formulate and quantify the trade-offs between privacy leakage, utility loss, and efficiency reduction, which leads us to the No-Free-Lunch (NFL) theorem for the federated learning system. NFL indicates that it is unrealistic to expect an FL algorithm to simultaneously provide excellent privacy, utility, and efficiency in certain scenarios. We then analyze the lower bounds for the privacy leakage, utility loss, and efficiency reduction for several widely-adopted protection mechanisms, including Randomization, Homomorphic Encryption, Secret Sharing, and Compression. Our analysis could serve as a guide for selecting protection parameters to meet particular requirements.

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TMLR Journal 2023 Journal Article

Understanding and Simplifying Architecture Search in Spatio-Temporal Graph Neural Networks

  • Zhen Xu
  • Quanming Yao
  • Yong Li
  • Qiang Yang

Compiling together spatial and temporal modules via a unified framework, Spatio-Temporal Graph Neural Networks (STGNNs) have been popularly used in the multivariate spatio-temporal forecasting task, e.g. traffic prediction. After the numerous propositions of manually designed architectures, researchers show interest in the Neural Architecture Search (NAS) of STGNNs. Existing methods suffer from two issues: (1) hyperparameters like learning rate, channel size cannot be integrated into the NAS framework, which makes the model evaluation less accurate, potentially misleading the architecture search (2) the current search space, which basically mimics Darts-like methods, is too large for the search algorithm to find a sufficiently good candidate. In this work, we deal with both issues at the same time. We first re-examine the importance and transferability of the training hyperparameters to ensure a fair and fast comparison. Next, we set up a framework that disentangles architecture design into three disjoint angles according to how spatio-temporal representations flow and transform in architectures, which allows us to understand the behavior of architectures from a distributional perspective. This way, we can obtain good guidelines to reduce the STGNN search space and find state-of-the-art architectures by simple random search. As an illustrative example, we combine these principles with random search which already significantly outperforms both state-of-the-art hand-designed models and recently automatically searched ones.

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TIST Journal 2022 Journal Article

Efficient Federated Matrix Factorization Against Inference Attacks

  • Di Chai
  • Leye Wang
  • Kai Chen
  • Qiang Yang

Recommender systems typically require the revelation of users’ ratings to the recommender server, which will subsequently use these ratings to provide personalized services. However, such revelations make users vulnerable to a broader set of inference attacks, allowing the recommender server to learn users’ private attributes, e.g., age and gender. Therefore, in this paper, we propose an efficient federated matrix factorization method that protects users against inference attacks. The key idea is that we obfuscate one user’s rating to another such that the private attribute leakage is minimized under the given distortion budget, which bounds the recommending loss and overhead of system efficiency. During the obfuscation, we apply differential privacy to control the information leakage between the users. We also adopt homomorphic encryption to protect the intermediate results during training. Our framework is implemented and tested on real-world datasets. The result shows that our method can reduce up to 16.7% of inference attack accuracy compared to using no privacy protections.

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IJCAI Conference 2022 Conference Paper

FedCG: Leverage Conditional GAN for Protecting Privacy and Maintaining Competitive Performance in Federated Learning

  • Yuezhou Wu
  • Yan Kang
  • Jiahuan Luo
  • Yuanqin He
  • Lixin Fan
  • Rong Pan
  • Qiang Yang

Federated learning (FL) aims to protect data privacy by enabling clients to build machine learning models collaboratively without sharing their private data. Recent works demonstrate that information exchanged during FL is subject to gradient-based privacy attacks and, consequently, a variety of privacy-preserving methods have been adopted to thwart such attacks. However, these defensive methods either introduce orders of magnitudes more computational and communication overheads (e. g. , with homomorphic encryption) or incur substantial model performance losses in terms of prediction accuracy (e. g. , with differential privacy). In this work, we propose FEDCG, a novel federated learning method that leverages conditional generative adversarial networks to achieve high-level privacy protection while still maintaining competitive model performance. FEDCG decomposes each client's local network into a private extractor and a public classifier and keeps the extractor local to protect privacy. Instead of exposing extractors, FEDCG shares clients' generators with the server for aggregating clients' shared knowledge aiming to enhance the performance of each client's local networks. Extensive experiments demonstrate that FEDCG can achieve competitive model performance compared with FL baselines, and privacy analysis shows that FEDCG has a high-level privacy-preserving capability.

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TIST Journal 2022 Journal Article

No Free Lunch Theorem for Security and Utility in Federated Learning

  • Xiaojin Zhang
  • Hanlin Gu
  • Lixin Fan
  • Kai Chen
  • Qiang Yang

In a federated learning scenario where multiple parties jointly learn a model from their respective data, there exist two conflicting goals for the choice of appropriate algorithms. On one hand, private and sensitive training data must be kept secure as much as possible in the presence of semi-honest partners; on the other hand, a certain amount of information has to be exchanged among different parties for the sake of learning utility. Such a challenge calls for the privacy-preserving federated learning solution, which maximizes the utility of the learned model and maintains a provable privacy guarantee of participating parties’ private data. This article illustrates a general framework that (1) formulates the trade-off between privacy loss and utility loss from a unified information-theoretic point of view, and (2) delineates quantitative bounds of the privacy-utility trade-off when different protection mechanisms including randomization, sparsity, and homomorphic encryption are used. It was shown that in general there is no free lunch for the privacy-utility trade-off, and one has to trade the preserving of privacy with a certain degree of degraded utility. The quantitative analysis illustrated in this article may serve as the guidance for the design of practical federated learning algorithms.

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IJCAI Conference 2022 Conference Paper

On the Channel Pruning using Graph Convolution Network for Convolutional Neural Network Acceleration

  • Di Jiang
  • Yuan Cao
  • Qiang Yang

Network pruning is considered efficient for sparsification and acceleration of Convolutional Neural Network (CNN) based models that can be adopted in re-source-constrained environments. Inspired by two popular pruning criteria, i. e. magnitude and similarity, this paper proposes a novel structural pruning method based on Graph Convolution Network (GCN) to further promote compression performance. The channel features are firstly extracted by Global Average Pooling (GAP) from a batch of samples, and a graph model for each layer is generated based on the similarity of features. A set of agents for individual CNN layers are implemented by GCN and utilized to synthesize comprehensive channel information and determine the pruning scheme for the overall CNN model. The training process of each agent is carried out using Reinforcement Learning (RL) to ensure their convergence and adaptability to various network architectures. The proposed solution is assessed based on a range of image classification datasets i. e. , CIFAR and Tiny-ImageNet. The numerical results indicate that the proposed pruning method outperforms the pure magnitude-based or similarity-based pruning solutions and other SOTA methods (e. g. , HRank and SCP). For example, the proposed method can prune VGG16 by removing 93% of the model parameters without any accuracy reduction in the CIFAR10 dataset.

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TIST Journal 2021 Journal Article

A GDPR-compliant Ecosystem for Speech Recognition with Transfer, Federated, and Evolutionary Learning

  • Di Jiang
  • Conghui Tan
  • Jinhua Peng
  • Chaotao Chen
  • Xueyang Wu
  • Weiwei Zhao
  • Yuanfeng Song
  • Yongxin Tong

Automatic Speech Recognition (ASR) is playing a vital role in a wide range of real-world applications. However, Commercial ASR solutions are typically “one-size-fits-all” products and clients are inevitably faced with the risk of severe performance degradation in field test. Meanwhile, with new data regulations such as the European Union’s General Data Protection Regulation (GDPR) coming into force, ASR vendors, which traditionally utilize the speech training data in a centralized approach, are becoming increasingly helpless to solve this problem, since accessing clients’ speech data is prohibited. Here, we show that by seamlessly integrating three machine learning paradigms (i.e., T ransfer learning, F ederated learning, and E volutionary learning (TFE)), we can successfully build a win-win ecosystem for ASR clients and vendors and solve all the aforementioned problems plaguing them. Through large-scale quantitative experiments, we show that with TFE, the clients can enjoy far better ASR solutions than the “one-size-fits-all” counterpart, and the vendors can exploit the abundance of clients’ data to effectively refine their own ASR products.

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AAAI Conference 2021 System Paper

A Health-friendly Speaker Verification System Supporting Mask Wearing

  • Chaotao Chen
  • Di Jiang
  • Jinhua Peng
  • Rongzhong Lian
  • Chen Jason Zhang
  • Qian Xu
  • Lixin Fan
  • Qiang Yang

We demonstrate a health-friendly speaker verification system for voice-based identity verification on mobile devices. The system is built upon a speech processing module, a ResNet-based local acoustic feature extractor and a multihead attention-based embedding layer, and is optimized under an additive margin softmax loss for discriminative speaker verification. It is shown that the system achieves superior performance no matter whether there is mask wearing or not. This characteristic is important for speaker verification services operating in regions affected by the raging coronavirus pneumonia. With this demonstration1, the audience will have an in-depth experience of how the accuracy of bio-metric verification and the personal health are simultaneously ensured. We wish that this demonstration would boost the development of next-generation bio-metric verification technologies.

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JMLR Journal 2021 Journal Article

FATE: An Industrial Grade Platform for Collaborative Learning With Data Protection

  • Yang Liu
  • Tao Fan
  • Tianjian Chen
  • Qian Xu
  • Qiang Yang

Collaborative and federated learning has become an emerging solution to many industrial applications where data values from different sites are exploit jointly with privacy protection. We introduce FATE, an industrial-grade project that supports enterprises and institutions to build machine learning models collaboratively at large-scale in a distributed manner. FATE supports a variety of secure computation protocols and machine learning algorithms, and features out-of-box usability with end-to-end building modules and visualization tools. Documentations are available at https://github.com/FederatedAI/FATE. Case studies and other information are available at https://www.fedai.org. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2021. ( edit, beta )

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TIST Journal 2021 Journal Article

Industrial Federated Topic Modeling

  • Di Jiang
  • Yongxin Tong
  • Yuanfeng Song
  • Xueyang Wu
  • Weiwei Zhao
  • Jinhua Peng
  • Rongzhong Lian
  • Qian Xu

Probabilistic topic modeling has been applied in a variety of industrial applications. Training a high-quality model usually requires a massive amount of data to provide comprehensive co-occurrence information for the model to learn. However, industrial data such as medical or financial records are often proprietary or sensitive, which precludes uploading to data centers. Hence, training topic models in industrial scenarios using conventional approaches faces a dilemma: A party (i.e., a company or institute) has to either tolerate data scarcity or sacrifice data privacy. In this article, we propose a framework named Industrial Federated Topic Modeling (iFTM), in which multiple parties collaboratively train a high-quality topic model by simultaneously alleviating data scarcity and maintaining immunity to privacy adversaries. iFTM is inspired by federated learning, supports two representative topic models (i.e., Latent Dirichlet Allocation and SentenceLDA) in industrial applications, and consists of novel techniques such as private Metropolis-Hastings, topic-wise normalization, and heterogeneous model integration. We conduct quantitative evaluations to verify the effectiveness of iFTM and deploy iFTM in two real-life applications to demonstrate its utility. Experimental results verify iFTM’s superiority over conventional topic modeling.

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IS Journal 2021 Journal Article

Secure Federated Matrix Factorization

  • Di Chai
  • Leye Wang
  • Kai Chen
  • Qiang Yang

To protect user privacy and meet law regulations, federated (machine) learning is obtaining vast interests in recent years. The key principle of federated learning is training a machine learning model without needing to know each user’s personal raw private data. In this article, we propose a secure matrix factorization framework under the federated learning setting, called FedMF. First, we design a user-level distributed matrix factorization framework where the model can be learned when each user only uploads the gradient information (instead of the raw preference data) to the server. While gradient information seems secure, we prove that it could still leak users’ raw data. To this end, we enhance the distributed matrix factorization framework with homomorphic encryption. We implement the prototype of FedMF and test it with a real movie rating dataset. Results verify the feasibility of FedMF. We also discuss the challenges for applying FedMF in practice for future research.

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IS Journal 2021 Journal Article

SecureBoost: A Lossless Federated Learning Framework

  • Kewei Cheng
  • Tao Fan
  • Yilun Jin
  • Yang Liu
  • Tianjian Chen
  • Dimitrios Papadopoulos
  • Qiang Yang

The protection of user privacy is an important concern in machine learning, as evidenced by the rolling out of the General Data Protection Regulation (GDPR) in the European Union (EU) in May 2018. The GDPR is designed to give users more control over their personal data, which motivates us to explore machine learning frameworks for data sharing that do not violate user privacy. To meet this goal, in this article, we propose a novel lossless privacy-preserving tree-boosting system known as SecureBoost in the setting of federated learning. SecureBoost first conducts entity alignment under a privacy-preserving protocol and then constructs boosting trees across multiple parties with a carefully designed encryption strategy. This federated learning system allows the learning process to be jointly conducted over multiple parties with common user samples but different feature sets, which corresponds to a vertically partitioned dataset. An advantage of SecureBoost is that it provides the same level of accuracy as the non -privacy-preserving approach while at the same time, reveals no information of each private data provider. We show that the SecureBoost framework is as accurate as other nonfederated gradient tree-boosting algorithms that require centralized data, and thus, it is highly scalable and practical for industrial applications such as credit risk analysis. To this end, we discuss information leakage during the protocol execution and propose ways to provably reduce it.

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TIST Journal 2021 Journal Article

StarFL: Hybrid Federated Learning Architecture for Smart Urban Computing

  • Anbu Huang
  • Yang Liu
  • Tianjian Chen
  • Yongkai Zhou
  • Quan Sun
  • Hongfeng Chai
  • Qiang Yang

From facial recognition to autonomous driving, Artificial Intelligence (AI) will transform the way we live and work over the next couple of decades. Existing AI approaches for urban computing suffer from various challenges, including dealing with synchronization and processing of vast amount of data generated from the edge devices, as well as the privacy and security of individual users, including their bio-metrics, locations, and itineraries. Traditional centralized-based approaches require data in each organization be uploaded to the central database, which may be prohibited by data protection acts, such as GDPR and CCPA. To decouple model training from the need to store the data in the cloud, a new training paradigm called Federated Learning (FL) is proposed. FL enables multiple devices to collaboratively learn a shared model while keeping the training data on devices locally, which can significantly mitigate privacy leakage risk. However, under urban computing scenarios, data are often communication-heavy, high-frequent, and asynchronized, posing new challenges to FL implementation. To handle these challenges, we propose a new hybrid federated learning architecture called StarFL. By combining with Trusted Execution Environment (TEE), Secure Multi-Party Computation (MPC), and (Beidou) satellites, StarFL enables safe key distribution, encryption, and decryption, and provides a verification mechanism for each participant to ensure the security of the local data. In addition, StarFL can provide accurate timestamp matching to facilitate synchronization of multiple clients. All these improvements make StarFL more applicable to the security-sensitive scenarios for the next generation of urban computing.

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IJCAI Conference 2020 Conference Paper

A De Novo Divide-and-Merge Paradigm for Acoustic Model Optimization in Automatic Speech Recognition

  • Conghui Tan
  • Di Jiang
  • Jinhua Peng
  • Xueyang Wu
  • Qian Xu
  • Qiang Yang

Due to the rising awareness of privacy protection and the voluminous scale of speech data, it is becoming infeasible for Automatic Speech Recognition (ASR) system developers to train the acoustic model with complete data as before. In this paper, we propose a novel Divide-and-Merge paradigm to solve salient problems plaguing the ASR field. In the Divide phase, multiple acoustic models are trained based upon different subsets of the complete speech data, while in the Merge phase two novel algorithms are utilized to generate a high-quality acoustic model based upon those trained on data subsets. We first propose the Genetic Merge Algorithm (GMA), which is a highly specialized algorithm for optimizing acoustic models but suffers from low efficiency. We further propose the SGD-Based Optimizational Merge Algorithm (SOMA), which effectively alleviates the efficiency bottleneck of GMA and maintains superior performance. Extensive experiments on public data show that the proposed methods can significantly outperform the state-of-the-art.

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IJCAI Conference 2020 Conference Paper

A Multi-player Game for Studying Federated Learning Incentive Schemes

  • Kang Loon Ng
  • Zichen Chen
  • Zelei Liu
  • Han Yu
  • Yang Liu
  • Qiang Yang

Federated Learning (FL) enables participants to "share'' their sensitive local data in a privacy preserving manner and collaboratively build machine learning models. In order to sustain long-term participation by high quality data owners (especially if they are businesses), FL systems need to provide suitable incentives. To design an effective incentive scheme, it is important to understand how FL participants respond under such schemes. This paper proposes FedGame, a multi-player game to study how FL participants make action selection decisions under different incentive schemes. It allows human players to role-play under various conditions. The decision-making processes can be analyzed and visualized to inform FL incentive mechanism design in the future.

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IS Journal 2020 Journal Article

A Secure Federated Transfer Learning Framework

  • Yang Liu
  • Yan Kang
  • Chaoping Xing
  • Tianjian Chen
  • Qiang Yang

Machine learning relies on the availability of vast amounts of data for training. However, in reality, data are mostly scattered across different organizations and cannot be easily integrated due to many legal and practical constraints. To address this important challenge in the field of machine learning, we introduce a new technique and framework, known as federated transfer learning (FTL), to improve statistical modeling under a data federation. FTL allows knowledge to be shared without compromising user privacy and enables complementary knowledge to be transferred across domains in a data federation, thereby enabling a target-domain party to build flexible and effective models by leveraging rich labels from a source domain. This framework requires minimal modifications to the existing model structure and provides the same level of accuracy as the nonprivacy-preserving transfer learning. It is flexible and can be effectively adapted to various secure multiparty machine learning tasks.

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IS Journal 2020 Journal Article

A Sustainable Incentive Scheme for Federated Learning

  • Han Yu
  • Zelei Liu
  • Yang Liu
  • Tianjian Chen
  • Mingshu Cong
  • Xi Weng
  • Dusit Niyato
  • Qiang Yang

In federated learning (FL), a federation distributedly trains a collective machine learning model by leveraging privacy preserving technologies. However, FL participants need to incur some cost for contributing to the FL models. The training and commercialization of the models will take time. Thus, there will be delays before the federation could pay back the participants. This temporary mismatch between contributions and rewards has not been accounted for by existing payoff-sharing schemes. To address this limitation, we propose the FL incentivizer (FLI). It dynamically divides a given budget in a context-aware manner among data owners in a federation by jointly maximizing the collective utility while minimizing the inequality among the data owners, in terms of the payoff received and the waiting time for receiving payoffs. Comparisons with five state-of-the-art payoff-sharing schemes show that FLI attracts high-quality data owners and achieves the highest expected revenue for a federation.

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NeurIPS Conference 2020 Conference Paper

Graph Random Neural Networks for Semi-Supervised Learning on Graphs

  • Wenzheng Feng
  • Jie Zhang
  • Yuxiao Dong
  • Yu Han
  • Huanbo Luan
  • Qian Xu
  • Qiang Yang
  • Evgeny Kharlamov

We study the problem of semi-supervised learning on graphs, for which graph neural networks (GNNs) have been extensively explored. However, most existing GNNs inherently suffer from the limitations of over-smoothing, non-robustness, and weak-generalization when labeled nodes are scarce. In this paper, we propose a simple yet effective framework—GRAPH RANDOM NEURAL NETWORKS (GRAND)—to address these issues. In GRAND, we first design a random propagation strategy to perform graph data augmentation. Then we leverage consistency regularization to optimize the prediction consistency of unlabeled nodes across different data augmentations. Extensive experiments on graph benchmark datasets suggest that GRAND significantly outperforms state-of- the-art GNN baselines on semi-supervised node classification. Finally, we show that GRAND mitigates the issues of over-smoothing and non-robustness, exhibiting better generalization behavior than existing GNNs. The source code of GRAND is publicly available at https: //github. com/Grand20/grand.

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IS Journal 2020 Journal Article

Introduction to the Special Issue on Federated Machine Learning

  • Yang Liu
  • Han Yu
  • Qiang Yang

The articles in this special section focus on federated machine learning, an emerging research paradigm focusing on solving data-silos challenges in real-world industrial applications. It is a broad discipline that touches many topics, including distributed and collaborative learning, privacy-preserving machine learning, edge computing, and data valuation, etc. Its interdisciplinary nature calls for collaborative efforts from a variety of fields to establish new protocols, frameworks and systems to address unique challenges, and open problems. These articles highlight a selection of high-quality and original works in this new area, including accepted papers to the 1st International Workshop on Federated Machine Learning in conjunction with IJCAI 2019.

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TIST Journal 2020 Journal Article

Transfer Learning with Dynamic Distribution Adaptation

  • Jindong Wang
  • Yiqiang Chen
  • Wenjie Feng
  • Han Yu
  • Meiyu Huang
  • Qiang Yang

Transfer learning aims to learn robust classifiers for the target domain by leveraging knowledge from a source domain. Since the source and the target domains are usually from different distributions, existing methods mainly focus on adapting the cross-domain marginal or conditional distributions. However, in real applications, the marginal and conditional distributions usually have different contributions to the domain discrepancy. Existing methods fail to quantitatively evaluate the different importance of these two distributions, which will result in unsatisfactory transfer performance. In this article, we propose a novel concept called Dynamic Distribution Adaptation (DDA), which is capable of quantitatively evaluating the relative importance of each distribution. DDA can be easily incorporated into the framework of structural risk minimization to solve transfer learning problems. On the basis of DDA, we propose two novel learning algorithms: (1) Manifold Dynamic Distribution Adaptation (MDDA) for traditional transfer learning, and (2) Dynamic Distribution Adaptation Network (DDAN) for deep transfer learning. Extensive experiments demonstrate that MDDA and DDAN significantly improve the transfer learning performance and set up a strong baseline over the latest deep and adversarial methods on digits recognition, sentiment analysis, and image classification. More importantly, it is shown that marginal and conditional distributions have different contributions to the domain divergence, and our DDA is able to provide good quantitative evaluation of their relative importance, which leads to better performance. We believe this observation can be helpful for future research in transfer learning.

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IJCAI Conference 2019 Conference Paper

Cross-City Transfer Learning for Deep Spatio-Temporal Prediction

  • Leye Wang
  • Xu Geng
  • Xiaojuan Ma
  • Feng Liu
  • Qiang Yang

Spatio-temporal prediction is a key type of tasks in urban computing, e. g. , traffic flow and air quality. Adequate data is usually a prerequisite, especially when deep learning is adopted. However, the development levels of different cities are unbalanced, and still many cities suffer from data scarcity. To address the problem, we propose a novel cross-city transfer learning method for deep spatio-temporal prediction tasks, called RegionTrans. RegionTrans aims to effectively transfer knowledge from a data-rich source city to a data-scarce target city. More specifically, we first learn an inter-city region matching function to match each target city region to a similar source city region. A neural network is designed to effectively extract region-level representation for spatio-temporal prediction. Finally, an optimization algorithm is proposed to transfer learned features from the source city to the target city with the region matching function. Using citywide crowd flow prediction as a demonstration experiment, we verify the effectiveness of RegionTrans. Results show that RegionTrans can outperform the state-of-the-art fine-tuning deep spatio-temporal prediction models by reducing up to 10. 7% prediction error.

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AAAI Conference 2019 Short Paper

Ethically Aligned Mobilization of Community Effort to Reposition Shared Bikes

  • Zelei Liu
  • Han Yu
  • Leye Wang
  • Liang Hu
  • Qiang Yang

We consider the problem of mobilizing community effort to reposition indiscriminantly parked shared bikes in urban environments through crowdsourcing. We propose an ethically aligned incentive optimization approach WSLS which maximizes the rate of success for bike repositioning while minimizing cost and prioritizing users’ wellbeing. Realistic simulations based on a dataset from Singapore demonstrate that WSLS significantly outperforms existing approaches.

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IJCAI Conference 2019 Conference Paper

Fair and Explainable Dynamic Engagement of Crowd Workers

  • Han Yu
  • Yang Liu
  • Xiguang Wei
  • Chuyu Zheng
  • Tianjian Chen
  • Qiang Yang
  • Xiong Peng

Years of rural-urban migration has resulted in a significant population in China seeking ad-hoc work in large urban centres. At the same time, many businesses face large fluctuations in demand for manpower and require more efficient ways to satisfy such demands. This paper outlines AlgoCrowd, an artificial intelligence (AI)-empowered algorithmic crowdsourcing platform. Equipped with an efficient explainable task-worker matching optimization approach designed to focus on fair treatment of workers while maximizing collective utility, the platform provides explainable task recommendations to workers' personal work management mobile apps which are becoming popular, with the aim to address the above societal challenge.

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TIST Journal 2019 Journal Article

Federated Machine Learning

  • Qiang Yang
  • Yang Liu
  • Tianjian Chen
  • Yongxin Tong

Today’s artificial intelligence still faces two major challenges. One is that, in most industries, data exists in the form of isolated islands. The other is the strengthening of data privacy and security. We propose a possible solution to these challenges: secure federated learning. Beyond the federated-learning framework first proposed by Google in 2016, we introduce a comprehensive secure federated-learning framework, which includes horizontal federated learning, vertical federated learning, and federated transfer learning. We provide definitions, architectures, and applications for the federated-learning framework, and provide a comprehensive survey of existing works on this subject. In addition, we propose building data networks among organizations based on federated mechanisms as an effective solution to allowing knowledge to be shared without compromising user privacy.

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IJCAI Conference 2019 Conference Paper

Multi-Agent Visualization for Explaining Federated Learning

  • Xiguang Wei
  • Quan Li
  • Yang Liu
  • Han Yu
  • Tianjian Chen
  • Qiang Yang

As an alternative decentralized training approach, Federated Learning enables distributed agents to collaboratively learn a machine learning model while keeping personal/private information on local devices. However, one significant issue of this framework is the lack of transparency, thus obscuring understanding of the working mechanism of Federated Learning systems. This paper proposes a multi-agent visualization system that illustrates what is Federated Learning and how it supports multi-agents coordination. To be specific, it allows users to participate in the Federated Learning empowered multi-agent coordination. The input and output of Federated Learning are visualized simultaneously, which provides an intuitive explanation of Federated Learning for users in order to help them gain deeper understanding of the technology.

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AAAI Conference 2019 Conference Paper

Multi-Fidelity Automatic Hyper-Parameter Tuning via Transfer Series Expansion

  • Yi-Qi Hu
  • Yang Yu
  • Wei-Wei Tu
  • Qiang Yang
  • Yuqiang Chen
  • Wenyuan Dai

Automatic machine learning (AutoML) aims at automatically choosing the best configuration for machine learning tasks. However, a configuration evaluation can be very time consuming particularly on learning tasks with large datasets. This limitation usually restrains derivative-free optimization from releasing its full power for a fine configuration search using many evaluations. To alleviate this limitation, in this paper, we propose a derivative-free optimization framework for AutoML using multi-fidelity evaluations. It uses many lowfidelity evaluations on small data subsets and very few highfidelity evaluations on the full dataset. However, the lowfidelity evaluations can be badly biased, and need to be corrected with only a very low cost. We thus propose the Transfer Series Expansion (TSE) that learns the low-fidelity correction predictor efficiently by linearly combining a set of base predictors. The base predictors can be obtained cheaply from down-scaled and experienced tasks. Experimental results on real-world AutoML problems verify that the proposed framework can accelerate derivative-free configuration search significantly by making use of the multi-fidelity evaluations.

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IJCAI Conference 2019 Conference Paper

Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction

  • Quanming Yao
  • Xiawei Guo
  • James Kwok
  • Weiwei Tu
  • Yuqiang Chen
  • Wenyuan Dai
  • Qiang Yang

To meet the standard of differential privacy, noise is usually added into the original data, which inevitably deteriorates the predicting performance of subsequent learning algorithms. In this paper, motivated by the success of improving predicting performance by ensemble learning, we propose to enhance privacy-preserving logistic regression by stacking. We show that this can be done either by sample-based or feature-based partitioning. However, we prove that when privacy-budgets are the same, feature-based partitioning requires fewer samples than sample-based one, and thus likely has better empirical performance. As transfer learning is difficult to be integrated with a differential privacy guarantee, we further combine the proposed method with hypothesis transfer learning to address the problem of learning across different organizations. Finally, we not only demonstrate the effectiveness of our method on two benchmark data sets, i. e. , MNIST and NEWS20, but also apply it into a real application of cross-organizational diabetes prediction from RUIJIN data set, where privacy is of a significant concern.

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AAMAS Conference 2019 Conference Paper

Social Mobilization to Reposition Indiscriminately Parked Shareable Bikes

  • Zelei Liu
  • Han Yu
  • Leye Wang
  • Liang Hu
  • Qiang Yang

With rapid growth of shareable bikes comes the problem of indiscriminately parked bikes blocking traffic. We propose a centralized pricing based dynamic incentive mechanism to mobilize the participants via crowdsourcing with regarding to reposition the indiscriminately parked bikes. We formalize the key component of the proposed incentive mechanism into two decision-making model: individual decision-making model Cost-refundable, Multiple Resources Constrained Multiple Armed Bandit (CRMR-MAB) and overall decision-making model multi-dimensional and multiple choice Knapsack problem (MMKP). We proposed a comprehensive decision algorithm GA-WSLS which combines the two. Realistic simulation based on real-world dataset from Singapore demonstrated significant advantages of the proposed approach over 7 existing approaches.

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AAAI Conference 2019 Conference Paper

Spatiotemporal Multi-Graph Convolution Network for Ride-Hailing Demand Forecasting

  • Xu Geng
  • Yaguang Li
  • Leye Wang
  • Lingyu Zhang
  • Qiang Yang
  • Jieping Ye
  • Yan Liu

Region-level demand forecasting is an essential task in ridehailing services. Accurate ride-hailing demand forecasting can guide vehicle dispatching, improve vehicle utilization, reduce the wait-time, and mitigate traffic congestion. This task is challenging due to the complicated spatiotemporal dependencies among regions. Existing approaches mainly focus on modeling the Euclidean correlations among spatially adjacent regions while we observe that non-Euclidean pair-wise correlations among possibly distant regions are also critical for accurate forecasting. In this paper, we propose the spatiotemporal multi-graph convolution network (ST-MGCN), a novel deep learning model for ride-hailing demand forecasting. We first encode the non-Euclidean pair-wise correlations among regions into multiple graphs and then explicitly model these correlations using multi-graph convolution. To utilize the global contextual information in modeling the temporal correlation, we further propose contextual gated recurrent neural network which augments recurrent neural network with a contextual-aware gating mechanism to re-weights different historical observations. We evaluate the proposed model on two real-world large scale ride-hailing demand datasets and observe consistent improvement of more than 10% over stateof-the-art baselines.

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IJCAI Conference 2018 Conference Paper

Building Ethics into Artificial Intelligence

  • Han Yu
  • Zhiqi Shen
  • Chunyan Miao
  • Cyril Leung
  • Victor R. Lesser
  • Qiang Yang

As artificial intelligence (AI) systems become increasingly ubiquitous, the topic of AI governance for ethical decision-making by AI has captured public imagination. Within the AI research community, this topic remains less familiar to many researchers. In this paper, we complement existing surveys, which largely focused on the psychological, social and legal discussions of the topic, with an analysis of recent advances in technical solutions for AI governance. By reviewing publications in leading AI conferences including AAAI, AAMAS, ECAI and IJCAI, we propose a taxonomy which divides the field into four areas: 1) exploring ethical dilemmas; 2) individual ethical decision frameworks; 3) collective ethical decision frameworks; and 4) ethics in human-AI interactions. We highlight the intuitions and key techniques used in each approach, and discuss promising future research directions towards successful integration of ethical AI systems into human societies.

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AAAI Conference 2018 Conference Paper

Hierarchical Attention Transfer Network for Cross-Domain Sentiment Classification

  • Zheng Li
  • Ying Wei
  • Yu Zhang
  • Qiang Yang

Cross-domain sentiment classification aims to leverage useful information in a source domain to help do sentiment classification in a target domain that has no or little supervised information. Existing cross-domain sentiment classification methods cannot automatically capture non-pivots, i. e. , the domainspecific sentiment words, and pivots, i. e. , the domain-shared sentiment words, simultaneously. In order to solve this problem, we propose a Hierarchical Attention Transfer Network (HATN) for cross-domain sentiment classification. The proposed HATN provides a hierarchical attention transfer mechanism which can transfer attentions for emotions across domains by automatically capturing pivots and non-pivots. Besides, the hierarchy of the attention mechanism mirrors the hierarchical structure of documents, which can help locate the pivots and non-pivots better. The proposed HATN consists of two hierarchical attention networks, with one named P-net aiming to find the pivots and the other named NP-net aligning the non-pivots by using the pivots as a bridge. Specifically, P-net firstly conducts individual attention learning to provide positive and negative pivots for NP-net. Then, Pnet and NP-net conduct joint attention learning such that the HATN can simultaneously capture pivots and non-pivots and realize transferring attentions for emotions across domains. Experiments on the Amazon review dataset demonstrate the effectiveness of HATN.

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NeurIPS Conference 2018 Conference Paper

Learning to Multitask

  • Yu Zhang
  • Ying Wei
  • Qiang Yang

Multitask learning has shown promising performance in many applications and many multitask models have been proposed. In order to identify an effective multitask model for a given multitask problem, we propose a learning framework called Learning to MultiTask (L2MT). To achieve the goal, L2MT exploits historical multitask experience which is organized as a training set consisting of several tuples, each of which contains a multitask problem with multiple tasks, a multitask model, and the relative test error. Based on such training set, L2MT first uses a proposed layerwise graph neural network to learn task embeddings for all the tasks in a multitask problem and then learns an estimation function to estimate the relative test error based on task embeddings and the representation of the multitask model based on a unified formulation. Given a new multitask problem, the estimation function is used to identify a suitable multitask model. Experiments on benchmark datasets show the effectiveness of the proposed L2MT framework.

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AAAI Conference 2018 Conference Paper

Personalizing a Dialogue System With Transfer Reinforcement Learning

  • Kaixiang Mo
  • Yu Zhang
  • Shuangyin Li
  • Jiajun Li
  • Qiang Yang

It is difficult to train a personalized task-oriented dialogue system because the data collected from each individual is often insufficient. Personalized dialogue systems trained on a small dataset is likely to overfit and make it difficult to adapt to different user needs. One way to solve this problem is to consider a collection of multiple users as a source domain and an individual user as a target domain, and to perform transfer learning from the source domain to the target domain. By following this idea, we propose a PErsonalized Task-oriented diALogue (PETAL) system, a transfer reinforcement learning framework based on POMDP, to construct a personalized dialogue system. The PETAL system first learns common dialogue knowledge from the source domain and then adapts this knowledge to the target domain. The proposed PETAL system can avoid the negative transfer problem by considering differences between the source and target users in a personalized Q-function. Experimental results on a real-world coffee-shopping data and simulation data show that the proposed PETAL system can learn optimal policies for different users, and thus effectively improve the dialogue quality under the personalized setting.

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AAAI Conference 2018 Conference Paper

SmartHS: An AI Platform for Improving Government Service Provision

  • Yongqing Zheng
  • Han Yu
  • Lizhen Cui
  • Chunyan Miao
  • Cyril Leung
  • Qiang Yang

Over the years, government service provision in China has been plagued by inefficiencies. Previous attempts to address this challenge following a toolbox e-government system model in China were not effective. In this paper, we report on a successful experience in improving government service provision in the domain of social insurance in Shandong Province, China. Through standardization of service work- flows following the Complete Contract Theory (CCT) and the infusion of an artificial intelligence (AI) engine to maximize the expected quality of service while reducing waiting time, the Smart Human-resource Services (SmartHS) platform transcends organizational boundaries and improves system efficiency. Deployments in 3 cities involving 2, 000 participating civil servants and close to 3 million social insurance service cases over a 1 year period demonstrated that SmartHS significantly improves user experience with roughly a third of the original front desk staff. This new AI-enhanced mode of operation is useful for informing current policy discussions in many domains of government service provision.

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AAAI Conference 2018 Conference Paper

Transferable Contextual Bandit for Cross-Domain Recommendation

  • Bo Liu
  • Ying Wei
  • Yu Zhang
  • Zhixian Yan
  • Qiang Yang

Traditional recommendation systems (RecSys) suffer from two problems: the exploitation-exploration dilemma and the cold-start problem. One solution to solving the exploitationexploration dilemma is the contextual bandit policy, which adaptively exploits and explores user interests. As a result, the contextual bandit policy achieves increased rewards in the long run. The contextual bandit policy, however, may cause the system to explore more than needed in the cold-start situations, which can lead to worse short-term rewards. Crossdomain RecSys methods adopt transfer learning to leverage prior knowledge in a source RecSys domain to jump start the cold-start target RecSys. To solve the two problems together, in this paper, we propose the first applicable transferable contextual bandit (TCB) policy for the cross-domain recommendation. TCB not only benefits the exploitation but also accelerates the exploration in the target RecSys. TCB’s exploration, in turn, helps to learn how to transfer between different domains. TCB is a general algorithm for both homogeneous and heterogeneous domains. We perform both theoretical regret analysis and empirical experiments. The empirical results show that TCB outperforms the state-of-the-art algorithms over time.

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IS Journal 2017 Journal Article

Big Data

  • Weike Pan
  • Qiang Yang
  • Charu Aggarwal
  • Christoph Koch

Big data has been an enabler for innovation, reconstruction, and advancement of most sectors of our society, and it's receiving continuous and growing attention from researchers and practitioners in academia, industry, and government. There are, however, still lots of challenges spanning from theoretical foundations, systems, and technology to data policy and standards. This special issue focuses on how big data cuts across systems and applications arenas, and the guest editors' introduction describes the five articles they selected out of 30 submitted to cover a wide spectrum of interesting topics, including feature selection for big data analytics, astronomical image analysis, large-scale network prediction, online URL filtering, and massive transaction clustering.

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IJCAI Conference 2017 Conference Paper

Deep Neural Networks for High Dimension, Low Sample Size Data

  • Bo Liu
  • Ying Wei
  • Yu Zhang
  • Qiang Yang

Deep neural networks (DNN) have achieved breakthroughs in applications with large sample size. However, when facing high dimension, low sample size (HDLSS) data, such as the phenotype prediction problem using genetic data in bioinformatics, DNN suffers from overfitting and high-variance gradients. In this paper, we propose a DNN model tailored for the HDLSS data, named Deep Neural Pursuit (DNP). DNP selects a subset of high dimensional features for the alleviation of overfitting and takes the average over multiple dropouts to calculate gradients with low variance. As the first DNN method applied on the HDLSS data, DNP enjoys the advantages of the high nonlinearity, the robustness to high dimensionality, the capability of learning from a small number of samples, the stability in feature selection, and the end-to-end training. We demonstrate these advantages of DNP via empirical results on both synthetic and real-world biological datasets.

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AAAI Conference 2017 Conference Paper

Distant Domain Transfer Learning

  • Ben Tan
  • Yu Zhang
  • Sinno Pan
  • Qiang Yang

In this paper, we study a novel transfer learning problem termed Distant Domain Transfer Learning (DDTL). Different from existing transfer learning problems which assume that there is a close relation between the source domain and the target domain, in the DDTL problem, the target domain can be totally different from the source domain. For example, the source domain classifies face images but the target domain distinguishes plane images. Inspired by the cognitive process of human where two seemingly unrelated concepts can be connected by learning intermediate concepts gradually, we propose a Selective Learning Algorithm (SLA) to solve the DDTL problem with supervised autoencoder or supervised convolutional autoencoder as a base model for handling different types of inputs. Intuitively, the SLA algorithm selects usefully unlabeled data gradually from intermediate domains as a bridge to break the large distribution gap for transferring knowledge between two distant domains. Empirical studies on image classification problems demonstrate the effectiveness of the proposed algorithm, and on some tasks the improvement in terms of the classification accuracy is up to 17% over “non-transfer” methods.

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IJCAI Conference 2017 Conference Paper

End-to-End Adversarial Memory Network for Cross-domain Sentiment Classification

  • Zheng Li
  • Yu Zhang
  • Ying Wei
  • Yuxiang Wu
  • Qiang Yang

Domain adaptation tasks such as cross-domain sentiment classification have raised much attention in recent years. Due to the domain discrepancy, a sentiment classifier trained in a source domain may not work well when directly applied to a target domain. Traditional methods need to manually select pivots, which behave in the same way for discriminative learning in both domains. Recently, deep learning methods have been proposed to learn a representation shared by domains. However, they lack the interpretability to directly identify the pivots. To address the problem, we introduce an end-to-end Adversarial Memory Network (AMN) for cross-domain sentiment classification. Unlike existing methods, our approach can automatically capture the pivots using an attention mechanism. Our framework consists of two parameter-shared memory networks: one is for sentiment classification and the other is for domain classification. The two networks are jointly trained so that the selected features minimize the sentiment classification error and at the same time make the domain classifier indiscriminative between the representations from the source or target domains. Moreover, unlike deep learning methods that cannot tell us which words are the pivots, our approach can offer a direct visualization of them. Experiments on the Amazon review dataset demonstrate that our approach can significantly outperform state-of-the-art methods.

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AAAI Conference 2017 Conference Paper

Learning Sparse Task Relations in Multi-Task Learning

  • Yu Zhang
  • Qiang Yang

In multi-task learning, when the number of tasks is large, pairwise task relations exhibit sparse patterns since usually a task cannot be helpful to all of the other tasks and moreover, sparse task relations can reduce the risk of overfitting compared with the dense ones. In this paper, we focus on learning sparse task relations. Based on a regularization framework which can learn task relations among multiple tasks, we propose a SParse covAriance based mulTi-taSk (SPATS) model to learn a sparse covariance by using the 1 regularization. The resulting objective function of the SPATS method is convex, which allows us to devise an alternating method to solve it. Moreover, some theoretical properties of the proposed model are studied. Experiments on synthetic and real-world datasets demonstrate the effectiveness of the proposed method.

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TIST Journal 2017 Journal Article

Transfer Learning for Behavior Ranking

  • Weike Pan
  • Qiang Yang
  • Yuchao Duan
  • Ben Tan
  • Zhong Ming

Intelligent recommendation has been well recognized as one of the major approaches to address the information overload problem in the big data era. A typical intelligent recommendation engine usually consists of three major components, that is, data as the main input, algorithms for preference learning, and system for user interaction and high-performance computation. We observe that the data (e.g., users’ behavior) are usually in different forms, such as examinations (e.g., browse and collection) and ratings, where the former are often much more abundant than the latter. Although the data are in different representations, they are both related to users’ true preferences and are also deemed complementary to each other for preference learning. However, very few ranking or recommendation algorithms have been developed to exploit such two types of user behavior. In this article, we focus on jointly modeling the examination behavior and rating behavior and develop a novel and efficient ranking-oriented recommendation algorithm accordingly. First, we formally define a new recommendation problem termed behavior ranking, which aims to build a ranking-oriented model by exploiting both the examination behavior and rating behavior. Second, we develop a simple and generic transfer to rank (ToR) algorithm for behavior ranking, which transfers knowledge of candidate items from a global preference learning task to a local preference learning task. Compared with the previous work on integrating heterogeneous user behavior, our ToR algorithm is the first ranking-oriented solution, which can effectively generate recommendations in a more direct manner than those regression-oriented methods. Extensive empirical studies show that our ToR algorithm performs significantly more accurately than the state-of-the-art methods in most cases. Furthermore, our ToR algorithm is very efficient in terms of the time complexity, which is similar to those for homogeneous user behavior alone.

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AAAI Conference 2017 Conference Paper

Transitive Hashing Network for Heterogeneous Multimedia Retrieval

  • Zhangjie Cao
  • Mingsheng Long
  • Jianmin Wang
  • Qiang Yang

Hashing is widely applied to large-scale multimedia retrieval due to the storage and retrieval efficiency. Crossmodal hashing enables efficient retrieval of one modality from database relevant to a query of another modality. Existing work on cross-modal hashing assumes that heterogeneous relationship across modalities is available for learning to hash. This paper relaxes this strict assumption by only requiring heterogeneous relationship in some auxiliary dataset different from the query or database domain. We design a novel hybrid deep architecture, transitive hashing network (THN), to jointly learn cross-modal correlation from the auxiliary dataset, and align the data distributions of the auxiliary dataset with that of the query or database domain, which generates compact transitive hash codes for efficient crossmodal retrieval. Comprehensive empirical evidence validates that the proposed THN approach yields state of the art retrieval performance on standard multimedia benchmarks, i. e. NUS-WIDE and ImageNet-YahooQA.

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IJCAI Conference 2017 Conference Paper

Understanding How Feature Structure Transfers in Transfer Learning

  • Tongliang Liu
  • Qiang Yang
  • Dacheng Tao

Transfer learning transfers knowledge across domains to improve the learning performance. Since feature structures generally represent the common knowledge across different domains, they can be transferred successfully even though the labeling functions across domains differ arbitrarily. However, theoretical justification for this success has remained elusive. In this paper, motivated by self-taught learning, we regard a set of bases as a feature structure of a domain if the bases can (approximately) reconstruct any observation in this domain. We propose a general analysis scheme to theoretically justify that if the source and target domains share similar feature structures, the source domain feature structure is transferable to the target domain, regardless of the change of the labeling functions across domains. The transferred structure is interpreted to function as a regularization matrix which benefits the learning process of the target domain task. We prove that such transfer enables the corresponding learning algorithms to be uniformly stable. Specifically, we illustrate the existence of feature structure transfer in two well-known transfer learning settings: domain adaptation and learning to learn.

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IJCAI Conference 2016 Conference Paper

Collaborative Evolution for User Profiling in Recommender Systems

  • Zhongqi Lu
  • Sinno Jialin Pan
  • Yong Li
  • Jie Jiang
  • Qiang Yang

Accurate user profiling is important for an online recommender system to provide proper personalized recommendations to its users. In many real-world scenarios, the user's interests towards the items may change over time. Therefore, a dynamic and evolutionary user profile is needed. In this work, we come up with a novel evolutionary view of user's profile by proposing a Collaborative Evolution (CE) model, which learns the evolution of user's profiles through the sparse historical data in recommender systems and outputs the prospective user profile of the future. To verify the effectiveness of the proposed model, we conduct experiments on a real-world dataset, which is obtained from the online shopping website of Tencent - www. 51buy. com and contains more than 1 million users' shopping records in a time span of more than 180 days. Experimental analyses demonstrate that our proposed CE model can be used to make better future recommendations compared to several state-of-the-art methods.

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AAAI Conference 2016 Conference Paper

Instilling Social to Physical: Co-Regularized Heterogeneous Transfer Learning

  • Ying Wei
  • Yin Zhu
  • Cane Leung
  • Yangqiu Song
  • Qiang Yang

Ubiquitous computing tasks, such as human activity recognition (HAR), are enabling a wide spectrum of applications, ranging from healthcare to environment monitoring. The success of a ubiquitous computing task relies on sufficient physical sensor data with groundtruth labels, which are always scarce due to the expensive annotating process. Meanwhile, social media platforms provide a lot of social or semantic context information. People share what they are doing and where they are frequently in the messages they post. This rich set of socially shared activities motivates us to transfer knowledge from social media to address the sparsity issue of labelled physical sensor data. In order to transfer the knowledge of social and semantic context, we propose a Co-Regularized Heterogeneous Transfer Learning (CoHTL) model, which builds a common semantic space derived from two heterogeneous domains. Our proposed method outperforms state-of-the-art methods on two ubiquitous computing tasks, namely human activity recognition and region function discovery.

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IJCAI Conference 2016 Conference Paper

Matrix Factorization+ for Movie Recommendation

  • Lili Zhao
  • Zhongqi Lu
  • Sinno Jialin Pan
  • Qiang Yang

We present a novel model for movie recommendations using additional visual features extracted from pictorial data like posters and still frames, to better understand movies. In particular, several context-based methods for recommendation are shown to be special cases of our proposed framework. Unlike existing context-based approaches, our method can be used to incorporate visual features - features that are lacking in existing context-based approaches for movie recommendations. In reality, movie posters and still frames provide us with rich knowledge for understanding movies, users' preferences as well. For instance, user may want to watch a movie at the minute when she/he finds some released posters or still frames attractive. Unfortunately, such unique features cannot be revealed from rating data or other form of context that being used in most of existing methods. In this paper, we take a step in this direction and investigate both low-level and high-level visual features from the movie posters and still frames for further improvement of recommendation methods. A comprehensive set of experiments on real world datasets shows that our approach leads to significant improvement over the state-of-the-art methods.

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AAAI Conference 2016 Conference Paper

Multi-Domain Active Learning for Recommendation

  • Zihan Zhang
  • Xiaoming Jin
  • Lianghao Li
  • Guiguang Ding
  • Qiang Yang

Recently, active learning has been applied to recommendation to deal with data sparsity on a single domain. In this paper, we propose an active learning strategy for recommendation to alleviate the data sparsity in a multi-domain scenario. Specifically, our proposed active learning strategy simultaneously consider both specific and independent knowledge over all domains. We use the expected entropy to measure the generalization error of the domain-specific knowledge and propose a variance-based strategy to measure the generalization error of the domain-independent knowledge. The proposed active learning strategy use a unified function to effectively combine these two measurements. We compare our strategy with five state-of-the-art baselines on five different multi-domain recommendation tasks, which are constituted by three real-world data sets. The experimental results show that our strategy performs significantly better than all the baselines and reduces human labeling efforts by at least 5. 6%, 8. 3%, 11. 8%, 12. 5% and 15. 4% on the five tasks, respectively.

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TIST Journal 2016 Journal Article

Telco User Activity Level Prediction with Massive Mobile Broadband Data

  • Chen Luo
  • Jia Zeng
  • Mingxuan Yuan
  • Wenyuan Dai
  • Qiang Yang

Telecommunication (telco) operators aim to provide users with optimized services and bandwidth in a timely manner. The goal is to increase user experience while retaining profit. To do this, knowing the changing behavior patterns of users through their activity levels in advance can be a great help for operators to adjust their management strategies and reduce operational risk. To achieve this goal, the operators can make use of knowledge discovered from telco’s historical mobile broadband (MBB) records to predict mobile access activity level at an early stage. In this article, we report our research in a real-world telco setting involving more than one million telco users. Our novel contribution includes representing users as documents containing a collection of changing spatiotemporal “words” that express user behavior. By extracting users’ space-time access records in MBB data, we use latent Dirichlet allocation (LDA) to learn user-specific compact topic features for user activity level prediction. We propose a scalable online expectation-maximization (OEM) algorithm that can scale LDA to massive MBB data, which is significantly faster than several state-of-the-art online LDA algorithms. Using these real-world MBB data, we confirm high performance in user activity level prediction. In addition, we show that the inferred topics indicate that future activity level anomalies correlate highly with early skewed bandwidth supply and demand relations. Thus, our prediction system can also guide the telco operators to balance the telecommunication network in terms of supply-demand relations, saving deployment costs and energy of cell towers in the future.

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AAAI Conference 2015 Conference Paper

Content-Based Collaborative Filtering for News Topic Recommendation

  • Zhongqi Lu
  • Zhicheng Dou
  • Jianxun Lian
  • Xing Xie
  • Qiang Yang

News recommendation has become a big attraction with which major Web search portals retain their users. Two effective approaches are Content-based Filtering and Collaborative Filtering, each serving a specific recommendation scenario. The Content-based Filtering approaches inspect rich contexts of the recommended items, while the Collaborative Filtering approaches predict the interests of long-tail users by collaboratively learning from interests of related users. We have observed empirically that, for the problem of news topic displaying, both the rich context of news topics and the long-tail users exist. Therefore, in this paper, we propose a Content-based Collaborative Filtering approach (CCF) to bring both Content-based Filtering and Collaborative Filtering approaches together. We found that combining the two is not an easy task, but the benefits of CCF are impressive. On one hand, CCF makes recommendations based on the rich contexts of the news. On the other hand, CCF collaboratively analyzes the scarce feedbacks from the long-tail users. We tailored this CCF approach for the news topic displaying on the Bing front page and demonstrated great gains in attracting users. In the experiments and analyses part of this paper, we discuss the performance gains and insights in news topic recommendation in Bing.

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AAAI Conference 2015 Conference Paper

Efficient Task Sub-Delegation for Crowdsourcing

  • Han Yu
  • Chunyan Miao
  • Zhiqi Shen
  • Cyril Leung
  • Yiqiang Chen
  • Qiang Yang

Reputation-based approaches allow a crowdsourcing system to identify reliable workers to whom tasks can be delegated. In crowdsourcing systems that can be modeled as multi-agent trust networks consist of resource constrained trustee agents (i. e. , workers), workers may need to further sub-delegate tasks to others if they determine that they cannot complete all pending tasks before the stipulated deadlines. Existing reputation-based decision-making models cannot help workers decide when and to whom to sub-delegate tasks. In this paper, we proposed a reputation aware task sub-delegation (RTS) approach to bridge this gap. By jointly considering a worker’s reputation, workload, the price of its effort and its trust relationships with others, RTS can be implemented as an intelligent agent to help workers make sub-delegation decisions in a distributed manner. The resulting task allocation maximizes social welfare through efficient utilization of the collective capacity of a crowd, and provides provable performance guarantees. Experimental comparisons with state-of-the-art approaches based on the Epinions trust network demonstrate significant advantages of RTS under high workload conditions.

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IJCAI Conference 2015 Conference Paper

Quantized Correlation Hashing for Fast Cross-Modal Search

  • Botong Wu
  • Qiang Yang
  • Wei-Shi Zheng
  • Yizhou Wang
  • Jingdong Wang

Cross-modal hashing is designed to facilitate fast search across domains. In this work, we present a cross-modal hashing approach, called quantized correlation hashing (QCH), which takes into consideration the quantization loss over domains and the relation between domains. Unlike previous approaches that separate the optimization of the quantizer independent of maximization of domain correlation, our approach simultaneously optimizes both processes. The underlying relation between the domains that describes the same objects is established via maximizing the correlation between the hash codes across the domains. The resulting multi-modal objective function is transformed to a unimodal formalization, which is optimized through an alternative procedure. Experimental results on three real world datasets demonstrate that our approach outperforms the state-of-the-art multi-modal hashing methods.

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AAAI Conference 2015 Conference Paper

RAIN: Social Role-Aware Information Diffusion

  • Yang Yang
  • Jie Tang
  • Cane Leung
  • Yizhou Sun
  • Qicong Chen
  • Juanzi Li
  • Qiang Yang

Information diffusion, which studies how information is propagated in social networks, has attracted considerable research effort recently. However, most existing approaches do not distinguish social roles that nodes may play in the diffusion process. In this paper, we study the interplay between users’ social roles and their influence on information diffusion. We propose a Role-Aware INformation diffusion model (RAIN) that integrates social role recognition and diffusion modeling into a unified framework. We develop a Gibbssampling based algorithm to learn the proposed model using historical diffusion data. The proposed model can be applied to different scenarios. For instance, at the micro-level, the proposed model can be used to predict whether an individual user will repost a specific message; while at the macro-level, we can use the model to predict the scale and the duration of a diffusion process. We evaluate the proposed model on a real social media data set. Our model performs much better in both micro- and macro-level prediction than several alternative methods.

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AAAI Conference 2015 Conference Paper

Spectral Label Refinement for Noisy and Missing Text Labels

  • Yangqiu Song
  • Chenguang Wang
  • Ming Zhang
  • Hailong Sun
  • Qiang Yang

With the recent growth of online content on the Web, there have been more user generated data with noisy and missing labels, e. g. , social tags and voted labels from Amazon’s Mechanical Turks. Most of machine learning methods, which require accurate label sets, could not be trusted when the label sets were yet unreliable. In this paper, we provide a text label refinement algorithm to adjust the labels for such noisy and missing labeled datasets. We assume that the labeled sets can be refined based on the labels with certain confidence, and the similarity between data being consistent with the labels. We propose a label smoothness ratio criterion to measure the smoothness of the labels and the consistency between labels and data. We demonstrate the effectiveness of the label refining algorithm on eight labeled document datasets, and validate that the results are useful for generating better labels.

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IS Journal 2014 Journal Article

Bird Flu Outbreak Prediction via Satellite Tracking

  • Yuanchun Zhou
  • Mingjie Tang
  • Weike Pan
  • Jinyan Li
  • Weihang Wang
  • Jing Shao
  • Liang Wu
  • Jianhui Li

Advanced satellite tracking technologies have collected huge amounts of wild bird migration data. Biologists use these data to understand dynamic migration patterns, study correlations between habitats, and predict global spreading trends of avian influenza. The research discussed here transforms the biological problem into a machine learning problem by converting wild bird migratory paths into graphs. H5N1 outbreak prediction is achieved by discovering weighted closed cliques from the graphs using the mining algorithm High-wEight cLosed cliquE miNing (HELEN). The learning algorithm HELEN-p then predicts potential H5N1 outbreaks at habitats. This prediction method is more accurate than traditional methods used on a migration dataset obtained through a real satellite bird-tracking system. Empirical analysis shows that H5N1 spreads in a manner of high-weight closed cliques and frequent cliques.

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AAAI Conference 2014 Conference Paper

Source Free Transfer Learning for Text Classification

  • Zhongqi Lu
  • Yin Zhu
  • Sinno Pan
  • Evan Xiang
  • Yujing Wang
  • Qiang Yang

Transfer learning uses relevant auxiliary data to help the learning task in a target domain where labeled data is usually insufficient to train an accurate model. Given appropriate auxiliary data, researchers have proposed many transfer learning models. How to find such auxiliary data, however, is of little research so far. In this paper, we focus on the problem of auxiliary data retrieval, and propose a transfer learning framework that effectively selects helpful auxiliary data from an open knowledge space (e. g. the World Wide Web). Because there is no need of manually selecting auxiliary data for different target domain tasks, we call our framework Source Free Transfer Learning (SFTL). For each target domain task, SFTL framework iteratively queries for the helpful auxiliary data based on the learned model and then updates the model using the retrieved auxiliary data. We highlight the automatic constructions of queries and the robustness of the SFTL framework. Our experiments on 20NewsGroup dataset and a Google search snippets dataset suggest that the framework is capable of achieving comparable performance to those state-of-the-art methods with dedicated selections of auxiliary data.

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AAAI Conference 2013 Conference Paper

Active Transfer Learning for Cross-System Recommendation

  • Lili Zhao
  • Sinno Pan
  • Evan Xiang
  • Erheng Zhong
  • Zhongqi Lu
  • Qiang Yang

Recommender systems, especially the newly launched ones, have to deal with the data-sparsity issue, where little existing rating information is available. Recently, transfer learning has been proposed to address this problem by leveraging the knowledge from related recommender systems where rich collaborative data are available. However, most previous transfer learning models assume that entity-correspondences across different systems are given as input, which means that for any entity (e. g. , a user or an item) in a target system, its corresponding entity in a source system is known. This assumption can hardly be satisfied in real-world scenarios where entity-correspondences across systems are usually unknown, and the cost of identifying them can be expensive. For example, it is extremely difficult to identify whether a user A from Facebook and a user B from Twitter are the same person. In this paper, we propose a framework to construct entity correspondence with limited budget by using active learning to facilitate knowledge transfer across recommender systems. Specifically, for the purpose of maximizing knowledge transfer, we first iteratively select entities in the target system based on our proposed criterion to query their correspondences in the source system. We then plug the actively constructed entity-correspondence mapping into a general transferred collaborative-filtering model to improve recommendation quality. We perform extensive experiments on real world datasets to verify the effectiveness of our proposed framework for this crosssystem recommendation problem.

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IJCAI Conference 2013 Conference Paper

Online Hashing

  • Long-Kai Huang
  • Qiang Yang
  • Wei-Shi Zheng

Hash function learning has been recently received more and more attentions in fast search for large scale data. However, existing popular learning based hashing methods are batch-based learning models and thus incur large scale computational problem for learning an optimal model on a large scale of labelled data and cannot handle data which comes sequentially. In this paper, we address the problem by developing an online hashing learning algorithm to get hashing model accommodate to each new pair of data. At the same time the new updated hash model is penalized by the last learned model in order to retain important information learned in previous rounds. We also derive a tight bound for the cumulative loss of our proposed online learning algorithm. The experimental results demonstrate superiority of the proposed online hashing model on searching both metric distance neighbors and semantical similar neighbors in the experiments.

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IJCAI Conference 2013 Conference Paper

Smart Hashing Update for Fast Response

  • Qiang Yang
  • Long-Kai Huang
  • Wei-Shi Zheng
  • Yingbiao Ling

Recent years have witnessed the growing popularity of hash function learning for large-scale data search. Although most existing hashing-based methods have been proven to obtain high accuracy, they are regarded as passive hashing and assume that the labelled points are provided in advance. In this paper, we consider updating a hashing model upon gradually increased labelled data in a fast response to users, called smart hashing update (SHU). In order to get a fast response to users, SHU aims to select a small set of hash functions to relearn and only updates the corresponding hash bits of all data points. More specifically, we put forward two selection methods for performing ef- ficient and effective update. In order to reduce the response time for acquiring a stable hashing algorithm, we also propose an accelerated method in order to further reduce interactions between users and the computer. We evaluate our proposals on two benchmark data sets. Our experimental results show it is not necessary to update all hash bits in order to adapt the model to new input data, and meanwhile we obtain better or similar performance without sacrificing much accuracy against the batch mode update.

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AAAI Conference 2012 Conference Paper

A Mouse-Trajectory Based Model for Predicting Query-URL Relevance

  • Song Hengjie
  • Ruoxue Liao
  • Xiangliang Zhang
  • Chunyan Miao
  • Qiang Yang

For the learning to ranking algorithms used in commercial search engines, a conventional way to generate the training examples is to employ professional annotators to label the relevance of query url pairs. Since label quality depends on the expertise of annotators to a large extent, this process is time consuming and labor intensive. Automatically generating labels from click through data has been well studied to have comparable or better performance than human judges. Click through data present users’ action and imply their satisfaction on search results, but exclude the interactions between users and search results beyond the page view level (e. g. , eye and mouse movements). This paper proposes a novel approach to comprehensively consider the information underlying mouse trajectory and click through data so as to describe user behaviors more objectively and achieve a better understanding of the user experience. By integrating multi sources data, the proposed approach reveals that the relevance labels of query url pairs are related to positions of urls and users’ behavioral features. Based on their correlations, query url pairs can be labeled more accurately and search results are more satisfactory to users. The experiments that are conducted on the most popular Chinese commercial search engine (Baidu) validated the rationality of our research motivation and proved that the proposed approach outperformed the state of the art methods.

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NeurIPS Conference 2012 Conference Paper

Action-Model Based Multi-agent Plan Recognition

  • Hankz Zhuo
  • Qiang Yang
  • Subbarao Kambhampati

Multi-Agent Plan Recognition (MAPR) aims to recognize dynamic team structures and team behaviors from the observed team traces (activity sequences) of a set of intelligent agents. Previous MAPR approaches required a library of team activity sequences (team plans) be given as input. However, collecting a library of team plans to ensure adequate coverage is often difficult and costly. In this paper, we relax this constraint, so that team plans are not required to be provided beforehand. We assume instead that a set of action models are available. Such models are often already created to describe domain physics; i. e. , the preconditions and effects of effects actions. We propose a novel approach for recognizing multi-agent team plans based on such action models rather than libraries of team plans. We encode the resulting MAPR problem as a \emph{satisfiability problem} and solve the problem using a state-of-the-art weighted MAX-SAT solver. Our approach also allows for incompleteness in the observed plan traces. Our empirical studies demonstrate that our algorithm is both effective and efficient in comparison to state-of-the-art MAPR methods based on plan libraries.

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AAAI Conference 2012 Conference Paper

Discovering Spammers in Social Networks

  • Yin Zhu
  • Xiao Wang
  • Erheng Zhong
  • Nathan Liu
  • He Li
  • Qiang Yang

As the popularity of the social media increases, as evidenced in Twitter, Facebook and China’s Renren, spamming activities also picked up in numbers and variety. On social network sites, spammers often disguise themselves by creating fake accounts and hijacking normal users’ accounts for personal gains. Different from the spammers in traditional systems such as SMS and email, spammers in social media behave like normal users and they continue to change their spamming strategies to fool anti-spamming systems. However, due to the privacy and resource concerns, many social media websites cannot fully monitor all the contents of users, making many of the previous approaches, such as topology-based and content-classification-based methods, infeasible to use. In this paper, we propose a Supervised Matrix Factorization method with Social Regularization (SMFSR) for spammer detection in social networks that exploits both social activities as well as users’ social relations in an innovative and highly scalable manner. The proposed method detects spammers collectively based on users’ social actions and social relations. We have empirically tested our method on data from Renren. com, which is one of the largest social networks in China, and demonstrated that our new method can improve the detection performance significantly.

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IS Journal 2012 Journal Article

SMS Spam Detection Using Noncontent Features

  • Qian Xu
  • Evan Wei Xiang
  • Qiang Yang
  • Jiachun Du
  • Jieping Zhong

Short Message Service text messages are indispensable, but they face a serious problem from spamming. This service-side solution uses graph data mining to distinguish spammers from nonspammers and detect spam without checking a message's contents.

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AAAI Conference 2012 Conference Paper

Transfer Learning in Collaborative Filtering with Uncertain Ratings

  • Weike Pan
  • Evan Xiang
  • Qiang Yang

To solve the sparsity problem in collaborative filtering, researchers have introduced transfer learning as a viable approach to make use of auxiliary data. Most previous transfer learning works in collaborative filtering have focused on exploiting point-wise ratings such as numerical ratings, stars, or binary ratings of likes/dislikes. However, in many real-world recommender systems, many users may be unwilling or unlikely to rate items with precision. In contrast, practitioners can turn to various non-preference data to estimate a range or rating distribution of a user’s preference on an item. Such a range or rating distribution is called an uncertain rating since it represents a rating spectrum of uncertainty instead of an accurate point-wise score. In this paper, we propose an efficient transfer learning solution for collaborative filtering, known as transfer by integrative factorization (TIF), to leverage such auxiliary uncertain ratings to improve the performance of recommendation. In particular, we integrate auxiliary data of uncertain ratings as additional constraints in the target matrix factorization problem, and learn an expected rating value for each uncertain rating automatically. The advantages of our proposed approach include the efficiency and the improved effectiveness of collaborative filtering, showing that incorporating the auxiliary data of uncertain ratings can really bring a benefit. Experimental results on two movie recommendation tasks show that our TIF algorithm performs significantly better over a state-ofthe-art non-transfer learning method.

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AAAI Conference 2012 Conference Paper

Transfer Learning with Graph Co-Regularization

  • Mingsheng Long
  • Jianmin Wang
  • Guiguang Ding
  • Dou Shen
  • Qiang Yang

Transfer learning proves to be effective for leveraging labeled data in the source domain to build an accurate classifier in the target domain. The basic assumption behind transfer learning is that the involved domains share some common latent factors. Previous methods usually explore these latent factors by optimizing two separate objective functions, i. e. , either maximizing the empirical likelihood, or preserving the geometric structure. Actually, these two objective functions are complementary to each other and optimizing them simultaneously can make the solution smoother and further improve the accuracy of the final model. In this paper, we propose a novel approach called Graph co-regularized Transfer Learning (GTL) for this purpose, which integrates the two objective functions seamlessly into one unified optimization problem. Thereafter, we present an iterative algorithm for the optimization problem with rigorous analysis on convergence and complexity. Our empirical study on two open data sets validates that GTL can consistently improve the classification accuracy compared to the state-of-the-art transfer learning methods.

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AAAI Conference 2011 Conference Paper

Active Dual Collaborative Filtering with Both Item and Attribute Feedback

  • Luheng He
  • Nathan Liu
  • Qiang Yang

The new user problem (aka user cold start) is very common in online recommender systems. Active collaborative filtering (active CF) tries to solve this problem by intelligently soliciting user feedback in order to build an initial user profile with minimal costs. Existing methods only query the user for feedback on items, while users can have preferences over items as well as certain item attributes. In this paper, we extend active CF via user feedback on both items and attributes. For example, when making movie recommendations, the system can ask users for not only their favorite movies, but also attributes such as genres, actors, etc. We design a unified active CF framework for incorporating both item and attribute feedback based on the random walk model. We test the active CF algorithm on realworld movie recommendation data sets to demonstrate that appropriately querying for both item and feature feedback can significantly reduce the overall user effort measured in terms of number of queries. We show that we can achieve much better recommendation quality as compared to traditional active CF methods that support only item feedback.

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IJCAI Conference 2011 Conference Paper

Distance Metric Learning under Covariate Shift

  • Bin Cao
  • Xiaochuan Ni
  • Jian-Tao Sun
  • Gang Wang
  • Qiang Yang

Learning distance metrics is a fundamental problem in machine learning. Previous distance-metric learning research assumes that the training and test data are drawn from the same distribution, which may be violated in practical applications. When the distributions differ, a situation referred to as covariate shift, the metric learned from training data may not work well on the test data. In this case the metric is said to be inconsistent. In this paper, we address this problem by proposing a novel metric learning framework known as consistent distance metric learning (CDML), which solves the problem under covariate shift situations. We theoretically analyze the conditions when the metrics learned under covariate shift are consistent. Based on the analysis, a convex optimization problem is proposed to deal with the CDML problem. An importance sampling method is proposed for metric learning and two importance weighting strategies are proposed and compared in this work. Experiments are carried out on synthetic and real world datasets to show the effectiveness of the proposed method.

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AAAI Conference 2011 Conference Paper

Heterogeneous Transfer Learning for Image Classification

  • Yin Zhu
  • Yuqiang Chen
  • Zhongqi Lu
  • Sinno Pan
  • Gui-Rong Xue
  • Yong Yu
  • Qiang Yang

Transfer learning as a new machine learning paradigm has gained increasing attention lately. In situations where the training data in a target domain are not sufficient to learn predictive models effectively, transfer learning leverages auxiliary source data from other related source domains for learning. While most of the existing works in this area only focused on using the source data with the same structure as the target data, in this paper, we push this boundary further by proposing a heterogeneous transfer learning framework for knowledge transfer between text and images. We observe that for a target-domain classification problem, some annotated images can be found on many social Web sites, which can serve as a bridge to transfer knowledge from the abundant text documents available over the Web. A key question is how to effectively transfer the knowledge in the source data even though the text can be arbitrarily found. Our solution is to enrich the representation of the target images with semantic concepts extracted from the auxiliary source data through a novel matrix factorization method. By using the latent semantic features generated by the auxiliary data, we are able to build a better integrated image classifier. We empirically demonstrate the effectiveness of our algorithm on the Caltech-256 image dataset.

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IJCAI Conference 2011 Conference Paper

Incorporating Reviewer and Product Information for Review Rating Prediction

  • Fangtao Li
  • Nathan Liu
  • Hongwei Jin
  • Kai Zhao
  • Qiang Yang
  • Xiaoyan Zhu

Among sentiment analysis tasks, review rating prediction is more helpful than binary (positive and negative) classification, especially when the consumers want to compare two good products. Previous work has addressed this problem by extracting various features from the review text for learning a predictor. Since the same word may have different sentiment effects when used by different reviewers on different products, we argue that it is necessary to model such reviewer and product dependent effects in order to predict review ratings more accurately. In this paper, we propose a novel learning framework to incorporate reviewer and product information into the text based learner for rating prediction. The reviewer, product and text feature are modeled as a three-dimension tensor. The tensor factorization technique is employed to reduce the sparsity and complexity problems. The experiment results demonstrate the effectiveness of our model. We achieve significant improvement as compared with the state of the art methods, especially for the reviews with unpopular products and inactive reviewers.

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AAMAS Conference 2011 Conference Paper

Learning Action Models for Multi-Agent Planning

  • Hankz Hankui Zhuo
  • Hector Mu
  • ntilde; oz-Avila
  • Qiang Yang

In multi-agent planning environments, action models for each agent must be given as input. However, creating such action models by hand is difficult and time-consuming, because it requires formally representing the complex relationships among different objects in the environment. The problem is compounded in multi-agent environments where agents can take more types of actions. In this paper, we present an algorithm to learn action models for multi-agent planning systems from a set of input plan traces. Our learning algorithm Lammas automatically generates three kinds of constraints: (1) constraints on the interactions between agents, (2) constraints on the correctness of the action models for each individual agent, and (3) constraints on actions themselves. Lammas attempts to satisfy these constraints simultaneously using a weighted maximum satisfiability model known as MAX-SAT, and converts the solution into action models. We believe this to be one of the first learning algorithms to learn action models in the context of multi-agent planning environments. We empirically demonstrate that Lammas performs effectively and efficiently in several planning domains.

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IJCAI Conference 2011 Conference Paper

Source-Selection-Free Transfer Learning

  • Evan Wei Xiang
  • Sinno Jialin Pan
  • Weike Pan
  • Jian Su
  • Qiang Yang

Transfer learning addresses the problems that labeled training data are insufficient to produce a high-performance model. Typically, given a target learning task, most transfer learning approaches require to select one or more auxiliary tasks as sources by the designers. However, how to select the right source data to enable effective knowledge transfer automatically is still an unsolved problem, which limits the applicability of transfer learning. In this paper, we take one step ahead and propose a novel transfer learning framework, known as source-selection-free transfer learning (SSFTL), to free users from the need to select source domains. Instead of asking the users for source and target data pairs, as traditional transfer learning does, SSFTL turns to some online information sources such as World Wide Web or the Wikipedia for help. The source data for transfer learning can be hidden somewhere within this large online information source, but the users do not know where they are. Based on the online information sources, we train a large number of classifiers. Then, given a target task, a bridge is built for labels of the potential source candidates and the target domain data in SSFTL via some large online social media with tag cloud as a label translator. An added advantage of SSFTL is that, unlike many previous transfer learning approaches, which are difficult to scale up to the Web scale, SSFTL is highly scalable and can offset much of the training work to offline stage. We demonstrate the effectiveness and efficiency of SSFTL through extensive experiments on several real-world datasets in text classification.

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AAAI Conference 2011 Conference Paper

Transfer Learning by Structural Analogy

  • Huayan Wang
  • Qiang Yang

Transfer learning allows knowledge to be extracted from auxiliary domains and be used to enhance learning in a target domain. For transfer learning to be successful, it is critical to find the similarity between auxiliary and target domains, even when such mappings are not obvious. In this paper, we present a novel algorithm for finding the structural similarity between two domains, to enable transfer learning at a structured knowledge level. In particular, we address the problem of how to learn a non-trivial structural similarity mapping between two different domains when they are completely different on the representation level. This problem is challenging because we cannot directly compare features across domains. Our algorithm extracts the structural features within each domain and then maps the features into the Reproducing Kernel Hilbert Space (RKHS), such that the “structural dependencies” of features across domains can be estimated by kernel matrices of the features within each domain. By treating the analogues from both domains as equivalent, we can transfer knowledge to achieve a better understanding of the domains and improved performance for learning. We validate our approach on synthetic and real-world datasets.

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IJCAI Conference 2011 Conference Paper

Transfer Learning for Activity Recognition via Sensor Mapping

  • Derek Hao Hu
  • Qiang Yang

Activity recognition aims to identify and predict human activities based on a series of sensor readings. In recent years, machine learning methods have become popular in solving activity recognition problems. A special difficulty for adopting machine learning methods is the workload to annotate a large number of sensor readings as training data. Labeling sensor readings for their corresponding activities is a time-consuming task. In practice, we often have a set of labeled training instances ready for an activity recognition task. If we can transfer such knowledge to a new activity recognition scenario that is different from, but related to, the source domain, it will ease our effort to perform manual labeling of training data for the new scenario. In this paper, we propose a transfer learning framework based on automatically learning a correspondence between different sets of sensors to solve this transfer-learning in activity recognition problem. We validate our framework on two different datasets and compare it against previous approaches of activity recognition, and demonstrate its effectiveness.

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IJCAI Conference 2011 Conference Paper

Transfer Learning to Predict Missing Ratings via Heterogeneous User Feedbacks

  • Weike Pan
  • Nathan N. Liu
  • Evan W. Xiang
  • Qiang Yang

Data sparsity due to missing ratings is a major challenge for collaborative filtering (CF) techniques in recommender systems. This is especially true for CF domains where the ratings are expressed numerically. We observe that, while we may lack the information in numerical ratings, we may have more data in the form of binary ratings. This is especially true when users can easily express themselves with their likes and dislikes for certain items. In this paper, we explore how to use the binary preference data expressed in the form of like/dislike to help reduce the impact of data sparsity of more expressive numerical ratings. We do this by transferring the rating knowledge from some auxiliary data source in binary form (that is, likes or dislikes), to a target numerical rating matrix. Our solution is to model both numerical ratings and like/dislike in a principled way, using a novel framework of Transfer by Collective Factorization (TCF). In particular, we construct the shared latent space collectively and learn the data-dependent effect separately. A major advantage of the TCF approach over previous collective matrix factorization (or bi-factorization) methods is that we are able to capture the data-dependent effect when sharing the data-independent knowledge, so as to increase the overall quality of knowledge transfer. Experimental results demonstrate the effectiveness of TCF at various sparsity levels as compared to several state-of-the-art methods.

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IJCAI Conference 2011 Conference Paper

User-Dependent Aspect Model for Collaborative Activity Recognition

  • Vincent W. Zheng
  • Qiang Yang

Activity recognition aims to discover one or more users' actions and goals based on sensor readings. In the real world, a single user's data are often insufficient for training an activity recognition model due to the data sparsity problem. This is especially true when we are interested in obtaining a personalized model. In this paper, we study how to collaboratively use different users' sensor data to train a model that can provide personalized activity recognition for each user. We propose a user-dependent aspect model for this collaborative activity recognition task. Our model introduces user aspect variables to capture the user grouping information, so that a target user can also benefit from her similar users in the same group to train the recognition model. In this way, we can greatly reduce the need for much valuable and expensive labeled data required in training the recognition model for each user. Our model is also capable of incorporating time information and handling new user in activity recognition. We evaluate our model on a real-world WiFi data set obtained from an indoor environment, and show that the proposed model can outperform several state-of-art baseline algorithms.

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AAAI Conference 2010 Conference Paper

Adaptive Transfer Learning

  • Bin Cao
  • Sinno Jialin Pan
  • Yu Zhang
  • Dit-Yan Yeung
  • Qiang Yang

Transfer learning aims at reusing the knowledge in some source tasks to improve the learning of a target task. Many transfer learning methods assume that the source tasks and the target task be related, even though many tasks are not related in reality. However, when two tasks are unrelated, the knowledge extracted from a source task may not help, and even hurt, the performance of a target task. Thus, how to avoid negative transfer and then ensure a “safe transfer” of knowledge is crucial in transfer learning. In this paper, we propose an Adaptive Transfer learning algorithm based on Gaussian Processes (AT-GP), which can be used to adapt the transfer learning schemes by automatically estimating the similarity between a source and a target task. The main contribution of our work is that we propose a new semi-parametric transfer kernel for transfer learning from a Bayesian perspective, and propose to learn the model with respect to the target task, rather than all tasks as in multi-task learning. We can formulate the transfer learning problem as a unified Gaussian Process (GP) model. The adaptive transfer ability of our approach is verified on both synthetic and real-world datasets.

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AAAI Conference 2010 Conference Paper

Clickthrough Log Analysis by Collaborative Ranking

  • Bin Cao
  • Dou Shen
  • Kuansan Wang
  • Qiang Yang

Analyzing clickthrough log data is important for improving search performance as well as understanding user behaviors. In this paper, we propose a novel collaborative ranking model to tackle two difficulties in analyzing clickthrough log. First, previous studies have shown that users tend to click topranked results even they are less relevant. Therefore, we use pairwise ranking relation to avoid the position bias in clicks. Second, since click data are extremely sparse with respect to each query or user, we construct a collaboration model to eliminate the sparseness problem. We also find that the proposed model and previous popular used click-based models address different aspects of clickthrough log data. We further propose a hybrid model that can achieve significant improvement compared to the baselines on a large-scale real world dataset.

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AAAI Conference 2010 Conference Paper

Collaborative Filtering Meets Mobile Recommendation: A User-Centered Approach

  • Vincent Zheng
  • Bin Cao
  • Yu Zheng
  • Xing Xie
  • Qiang Yang

With the increasing popularity of location tracking services such as GPS, more and more mobile data are being accumulated. Based on such data, a potentially useful service is to make timely and targeted recommendations for users on places where they might be interested to go and activities that they are likely to conduct. For example, a user arriving in Beijing might wonder where to visit and what she can do around the Forbidden City. A key challenge for such recommendation problems is that the data we have on each individual user might be very limited, while to make useful and accurate recommendations, we need extensive annotated location and activity information from user trace data. In this paper, we present a new approach, known as user-centered collaborative location and activity filtering (UCLAF), to pull many users’ data together and apply collaborative filtering to find like-minded users and like-patterned activities at different locations. We model the userlocation-activity relations with a tensor representation, and propose a regularized tensor and matrix decomposition solution which can better address the sparse data problem in mobile information retrieval. We empirically evaluate UCLAF using a real-world GPS dataset collected from 164 users over 2. 5 years, and showed that our system can outperform several state-of-the-art solutions to the problem.

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IS Journal 2010 Journal Article

Social Learning

  • Qiang Yang
  • Zhi-Hua Zhou
  • Wenji Mao
  • Wei Li
  • Nathan Nan Liu

In recent years, social behavioral data have been exponentially expanding due to the tremendous success of various outlets on the social Web (aka Web 2. 0) such as Facebook, Digg, Twitter, Wikipedia, and Delicious. As a result, there's a need for social learning to support the discovery, analysis, and modeling of human social behavioral data. The goal is to discover social intelligence, which encompasses a spectrum of knowledge that characterizes human interaction, communication, and collaborations. The social Web has thus become a fertile ground for machine learning and data mining research. This special issue gathers the state-of-the-art research in social learning and is devoted to exhibiting some of the best representative works in this area.

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AAAI Conference 2010 Conference Paper

Transfer Learning in Collaborative Filtering for Sparsity Reduction

  • Weike Pan
  • Evan Xiang
  • Nathan Liu
  • Qiang Yang

Data sparsity is a major problem for collaborative filtering (CF) techniques in recommender systems, especially for new users and items. We observe that, while our target data are sparse for CF systems, related and relatively dense auxiliary data may already exist in some other more mature application domains. In this paper, we address the data sparsity problem in a target domain by transferring knowledge about both users and items from auxiliary data sources. We observe that in different domains the user feedbacks are often heterogeneous such as ratings vs. clicks. Our solution is to integrate both user and item knowledge in auxiliary data sources through a principled matrix-based transfer learning framework that takes into account the data heterogeneity. In particular, we discover the principle coordinates of both users and items in the auxiliary data matrices, and transfer them to the target domain in order to reduce the effect of data sparsity. We describe our method, which is known as coordinate system transfer or CST, and demonstrate its effectiveness in alleviating the data sparsity problem in collaborative filtering. We show that our proposed method can significantly outperform several state-of-the-art solutions for this problem.

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AAAI Conference 2010 Conference Paper

Visual Contextual Advertising: Bringing Textual Advertisements to Images

  • Yuqiang Chen
  • Ou Jin
  • Gui-Rong Xue
  • Jia Chen
  • Qiang Yang

Advertising in the case of textual Web pages has been studied extensively by many researchers. However, with the increasing amount of multimedia data such as image, audio and video on the Web, the need for recommending advertisement for the multimedia data is becoming a reality. In this paper, we address the novel problem of visual contextual advertising, which is to directly advertise when users are viewing images which do not have any surrounding text. A key challenging issue of visual contextual advertising is that images and advertisements are usually represented in image space and word space respectively, which are quite different with each other inherently. As a result, existing methods for Web page advertising are inapplicable since they represent both Web pages and advertisement in the same word space. In order to solve the problem, we propose to exploit the social Web to link these two feature spaces together. In particular, we present a unified generative model to integrate advertisements, words and images. Specifically, our solution combines two parts in a principled approach: First, we transform images from a image feature space to a word space utilizing the knowledge from images with annotations from social Web. Then, a language model based approach is applied to estimate the relevance between transformed images and advertisements. Moreover, in this model, the probability of recommending an advertisement can be inferred efficiently given an image, which enables potential applications to online advertising.

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IJCAI Conference 2009 Conference Paper

  • Hankz Hankui Zhuo
  • Derek Hao Hu
  • Chad Hogg
  • Qiang Yang
  • Hector Munoz-Avila

To apply hierarchical task network (HTN) planning to real-world planning problems, one needs to encode the HTN schemata and action models beforehand. However, acquiring such domain knowledge is difficult and time-consuming because the HTN domain definition involves a significant knowledge-engineering effort. A system that can learn the HTN planning domain knowledge automatically would save time and allow HTN planning to be used in domains where such knowledgeengineering effort is not feasible. In this paper, we present a formal framework and algorithms to acquire HTN planning domain knowledge, by learning the preconditions and effects of actions and preconditions of methods. Our algorithm, HTNlearner, first builds constraints from given observed decomposition trees to build action models and method preconditions. It then solves these constraints using a weighted MAX-SAT solver. The solution can be converted to action models and method preconditions. Unlike prior work on HTN learning, we do not depend on complete action models or state information. We test the algorithm on several domains, and show that our HTN-learner algorithm is both effective and efficient.

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IJCAI Conference 2009 Conference Paper

  • Bin Li
  • Qiang Yang
  • Xiangyang Xue

The sparsity problem in collaborative filtering (CF) is a major bottleneck for most CF methods. In this paper, we consider a novel approach for alleviating the sparsity problem in CF by transferring useritem rating patterns from a dense auxiliary rating matrix in other domains (e. g. , a popular movie rating website) to a sparse rating matrix in a target domain (e. g. , a new book rating website). We do not require that the users and items in the two domains be identical or even overlap. Based on the limited ratings in the target matrix, we establish a bridge between the two rating matrices at a clusterlevel of user-item rating patterns in order to transfer more useful knowledge from the auxiliary task domain. We first compress the ratings in the auxiliary rating matrix into an informative and yet compact cluster-level rating pattern representation referred to as a codebook. Then, we propose an efficient algorithm for reconstructing the target rating matrix by expanding the codebook. We perform extensive empirical tests to show that our method is effective in addressing the data sparsity problem by transferring the useful knowledge from the auxiliary tasks, as compared to many state-of-the-art CF methods.

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IJCAI Conference 2009 Conference Paper

  • Derek Hao Hu
  • Xian-Xing Zhang
  • Jie Yin
  • Vincent Wenchen Zheng
  • Qiang Yang

Detecting abnormal activities from sensor readings is an important research problem in activity recognition. A number of different algorithms have been proposed in the past to tackle this problem. Many of the previous state-based approaches suffer from the problem of failing to decide the appropriate number of states, which are difficult to find through a trial-and-error approach, in real-world applications. In this paper, we propose an accurate and flexible framework for abnormal activity recognition from sensor readings that involves less human tuning of model parameters. Our approach first applies a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM), which supports an infinite number of states, to automatically find an appropriate number of states. We incorporate a Fisher Kernel into the One-Class Support Vector Machine (OCSVM) model to filter out the activities that are likely to be normal. Finally, we derive an abnormal activity model from the normal activity models to reduce false positive rate in an unsupervised manner. Our main contribution is that our proposed HDP-HMM models can decide the appropriate number of states automatically, and that by incorporating a Fisher Kernel into the OCSVM model, we can combine the advantages from generative model and discriminative model. We demonstrate the effectiveness of our approach by using several real-world datasets to test our algorithm’s performance.

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IJCAI Conference 2009 Conference Paper

  • Sinno Jialin Pan
  • Ivor W. Tsang
  • James T. Kwok
  • Qiang Yang

Domain adaptation solves a learning problem in a target domain by utilizing the training data in a different but related source domain. Intuitively, discovering a good feature representation across domains is crucial. In this paper, we propose to find such a representation through a new learning method, transfer component analysis (TCA), for domain adaptation. TCA tries to learn some transfer components across domains in a Reproducing Kernel Hilbert Space (RKHS) using Maximum Mean Discrepancy (MMD). In the subspace spanned by these transfer components, data distributions in different domains are close to each other. As a result, with the new representations in this subspace, we can apply standard machine learning methods to train classifiers or regression models in the source domain for use in the target domain. The main contribution of our work is that we propose a novel feature representation in which to perform domain adaptation via a new parametric kernel using feature extraction methods, which can dramatically minimize the distance between domain distributions by projecting data onto the learned transfer components. Furthermore, our approach can handle large datsets and naturally lead to out-of-sample generalization. The effectiveness and efficiency of our approach in are verified by experiments on two real-world applications: cross-domain indoor WiFi localization and cross-domain text classification.

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IJCAI Conference 2009 Conference Paper

  • Qiang Yang

Sensors provide computer systems with a window to the outside world. Activity recognition “sees” what is in the window to predict the locations, trajectories, actions, goals and plans of humans and objects. Building an activity recognition system requires a full range of interaction from statistical inference on lower level sensor data to symbolic AI at higher levels, where prediction results and acquired knowledge are passed up each level to form a knowledge food chain. In this article, I will give an overview of some of the current activity recognition research works and explore a life-cycle of learning and inference that allows the lowestlevel radio-frequencysignals to be transformed into symbolic logical representations for AI planning, which in turn controls the robots or guides human users through a sensor network, thus completing a full life cycle of knowledge.

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IJCAI Conference 2009 Conference Paper

  • Jie Yin
  • Derek Hao Hu
  • Qiang Yang

Event detection is a critical task in sensor networks for a variety of real-world applications. Many realworld events often exhibit complex spatio-temporal patterns whereby they manifest themselves via observations over time and space proximities. These spatio-temporal events cannot be handled well by many of the previous approaches. In this paper, we propose a new Spatio-Temporal Event Detection (STED) algorithm in sensor networks based on a dynamic conditional random field (DCRF) model. Our STED method handles the uncertainty of sensor data explicitly and permits neighborhood interactions in both observations and event labels. Experiments on both real data and synthetic data demonstrate that our STED method can provide accurate event detection in near real time even for large-scale sensor networks.

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IS Journal 2008 Journal Article

Estimating Location Using Wi-Fi

  • Qiang Yang
  • Sinno Jialin Pan
  • Vincent Wenchen Zheng

This department presents the results of the first Data Mining Contest held at the 2007 International Conference on Data Mining.

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AAAI Conference 2008 Conference Paper

Transferring Localization Models over Time

  • Vincent Wenchen Zheng
  • Qiang Yang

Learning-based localization methods typically consist of an offline phase to collect the wireless signal data to build a statistical model, and an online phase to apply the model on new data. Many of these methods treat the training data as if their distributions are fixed across time. However, due to complex environmental changes such as temperature changes and multi-path fading effect, the signals can significantly vary from time to time, causing the localization accuracy to drop. We address this problem by introducing a novel semi-supervised Hidden Markov Model (HMM) to transfer the learned model from one time period to another. This adaptive model is referred to as transferred HMM (TrHMM), in which we aim to transfer as much knowledge from the old model as possible to reduce the calibration effort for the current time period. Our contribution is that we can successfully transfer out-of-date model to fit a current model through learning, even though the training data have very different distributions. Experimental results show that the TrHMM method can greatly improve the localization accuracy while saving a great amount of the calibration effort.

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NeurIPS Conference 2008 Conference Paper

Translated Learning: Transfer Learning across Different Feature Spaces

  • Wenyuan Dai
  • Yuqiang Chen
  • Gui-Rong Xue
  • Qiang Yang
  • Yong Yu

This paper investigates a new machine learning strategy called translated learning. Unlike many previous learning tasks, we focus on how to use labeled data from one feature space to enhance the classification of other entirely different learning spaces. For example, we might wish to use labeled text data to help learn a model for classifying image data, when the labeled images are difficult to obtain. An important aspect of translated learning is to build a "bridge" to link one feature space (known as the "source space") to another space (known as the "target space") through a translator in order to migrate the knowledge from source to target. The translated learning solution uses a language model to link the class labels to the features in the source spaces, which in turn is translated to the features in the target spaces. Finally, this chain of linkages is completed by tracing back to the instances in the target spaces. We show that this path of linkage can be modeled using a Markov chain and risk minimization. Through experiments on the text-aided image classification and cross-language classification tasks, we demonstrate that our translated learning framework can greatly outperform many state-of-the-art baseline methods.

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IJCAI Conference 2007 Conference Paper

  • Jeffrey Junfeng Pan
  • Qiang Yang

This paper addresses the problem of recovering the locations of both mobile devices and access points from radio signals, a problem which we call co-localization, by exploiting both labeled and unlabeled data from mobile devices and access points. We first propose a solution using Latent Semantic Indexing to construct the relative locations of the mobile devices and access points when their absolute locations are unknown. We then propose a semi-supervised learning algorithm based on manifold to obtain the absolute locations of the devices. Both solutions are finally combined together in terms of graph Laplacian. Extensive experiments are conducted in wireless local-area networks, wireless sensor networks and radio frequency identification networks. The experimental results show that we can achieve high accuracy with much less calibration effort as compared to several previous systems.

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IJCAI Conference 2007 Conference Paper

  • Bin Cao
  • Dou Shen
  • Jian-Tao Sun
  • Xuanhui Wang
  • Qiang Yang
  • Zheng Chen

Detecting and tracking latent factors from temporal data is an important task. Most existing algorithms for latent topic detection such as Nonnegative Matrix Factorization (NMF) have been designed for static data. These algorithms are unable to capture the dynamic nature of temporally changing data streams. In this paper, we put forward an online NMF (ONMF) algorithm to detect latent factors and track their evolution while the data evolve. By leveraging the already detected latent factors and the newly arriving data, the latent factors are automatically and incrementally updated to reflect the change of factors. Furthermore, by imposing orthogonality on the detected latent factors, we can not only guarantee the unique solution of NMF but also alleviate the partial-data problem, which may cause NMF to fail when the data are scarce or the distribution is incomplete. Experiments on both synthesized data and real data validate the efficiency and effectiveness of our ONMF algorithm.

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IJCAI Conference 2007 Conference Paper

  • Dou Shen
  • Jian-Tao Sun
  • Hua Li
  • Qiang Yang
  • Zheng Chen

Many methods, including supervised and unsupervised algorithms, have been developed for extractive document summarization. Most supervised methods consider the summarization task as a two-class classification problem and classify each sentence individually without leveraging the relationship among sentences. The unsupervised methods use heuristic rules to select the most informative sentences into a summary directly, which are hard to generalize. In this paper, we present a Conditional Random Fields (CRF) based framework to keep the merits of the above two kinds of approaches while avoiding their disadvantages. What is more, the proposed framework can take the outcomes of previous methods as features and seamlessly integrate them. The key idea of our approach is to treat the summarization task as a sequence labeling problem. In this view, each document is a sequence of sentences and the summarization procedure labels the sentences by 1 and 0. The label of a sentence depends on the assignment of labels of others. We compared our proposed approach with eight existing methods on an open benchmark data set. The results show that our approach can improve the performance by more than 7. 1% and 12. 1% over the best supervised baseline and unsupervised baseline respectively in terms of two popular metrics F1 and ROUGE-2. Detailed analysis of the improvement is presented as well.

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AAAI Conference 2007 Conference Paper

Adaptive Localization in a Dynamic WiFi Environment through Multi-view Learning

  • Sinno Jialin Pan
  • Qiang Yang

Accurately locating users in a wireless environment is an important task for many pervasive computing and AI applications, such as activity recognition. In a WiFi environment, a mobile device can be localized using signals received from various transmitters, such as access points (APs). Most localization approaches build a map between the signal space and the physical location space in a offline phase, and then using the received-signal-strength (RSS) map to estimate the location in an online phase. However, the map can be outdated when the signal-strength values change with time due to environmental dynamics. It is infeasible or expensive to repeat data calibration for reconstructing the RSS map. In such a case, it is important to adapt the model learnt in one time period to another time period without too much recalibration. In this paper, we present a location-estimation approach based on Manifold co-Regularization, which is a machine learning technique for building a mapping function between data. We describe LeManCoR, a system for adapting the mapping function between the signal space and physical location space over different time periods based on Manifold Co-Regularization. We show that LeManCoR can effectively transfer the knowledge between two time periods without requiring too much new calibration effort. We illustrate LeMan- CoR’s effectiveness in a real 802. 11 WiFi environment.

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KER Journal 2007 Journal Article

ARMS: an automatic knowledge engineering tool for learning action models for AI planning

  • KANGHENG WU
  • Qiang Yang
  • YUNFEI JIANG

Abstract We present an action model learning system known as ARMS (Action-Relation Modelling System) for automatically discovering action models from a set of successfully observed plans. Current artificial intelligence (AI) planners show impressive performance in many real world and artificial domains, but they all require the definition of an action model. ARMS is aimed at automatically learning action models from observed example plans, where each example plan is a sequence of action traces. These action models can then be used by the human editors to refine. The expectation is that this system will lessen the burden of the human editors in designing action models from scratch. In this paper, we describe the ARMS in detail. To learn action models, ARMS gathers knowledge on the statistical distribution of frequent sets of actions in the example plans. It then builds a weighted propositional satisfiability (weighted SAT) problem and solves it using a weighted MAXSAT solver. Furthermore, we show empirical evidence that ARMS can indeed learn a good approximation of the finally action models effectively.

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IS Journal 2007 Journal Article

Cost-Sensitive-Data Preprocessing for Mining Customer Relationship Management Databases

  • Junfeng Pan
  • Qiang Yang
  • Yiming Yang
  • Lei Li
  • Frances Li
  • George Li

A staged-framework for data preprocessing has been developed to support data mining and help service providers identify customers who might switch to a competitor. The framework pushes the cost sensitivity and data imbalance of customer retention data into the data preprocessing itself. Tests using data set from the ACM KDD Cup 1998 showed that the framework outperformed the winner of that data mining and knowledge discovery competition. The framework has also been incorporated into a software system, called ED-Money. To demonstrate the framework's ability to predict customer attrition with high accuracy, it was applied to some benchmark data and to a real customer attrition data set from a large Chinese mobile telecommunications company

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IS Journal 2007 Journal Article

Domain-Driven, Actionable Knowledge Discovery

  • Longbing Cao
  • Chengqi Zhang
  • Qiang Yang
  • David Bell
  • Michail Vlachos
  • Bahar Taneri
  • Eamonn Keogh
  • Philip S. Yu

Data mining increasingly faces complex challenges in the real-life world of business problems and needs. The gap between business expectations and R&D results in this area involves key aspects of the field, such as methodologies, targeted problems, pattern interestingness, and infrastructure support. Both researchers and practitioners are realizing the importance of domain knowledge to close this gap and develop actionable knowledge for real user needs.

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AAAI Conference 2007 Conference Paper

Transferring Naive Bayes Classifiers for Text Classification

  • Wenyuan Dai
  • Qiang Yang

A basic assumption in traditional machine learning is that the training and test data distributions should be identical. This assumption may not hold in many situations in practice, but we may be forced to rely on a different-distribution data to learn a prediction model. For example, this may be the case when it is expensive to label the data in a domain of interest, although in a related but different domain there may be plenty of labeled data available. In this paper, we propose a novel transfer-learning algorithm for text classification based on an EM-based Naive Bayes classifiers. Our solution is to first estimate the initial probabilities under a distribution D of one labeled data set, and then use an EM algorithm to revise the model for a different distribution Du of the test data which are unlabeled. We show that our algorithm is very effective in several different pairs of domains, where the distances between the different distributions are measured using the Kullback- Leibler (KL) divergence. Moreover, KL-divergence is used to decide the trade-off parameters in our algorithm. In the experiment, our algorithm outperforms the traditional supervised and semi-supervised learning algorithms when the distributions of the training and test sets are increasingly different.

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IJCAI Conference 2005 Conference Paper

Accurate and Low-cost Location Estimation Using Kernels

  • Jeffrey Junfeng Pan
  • James T. Kwok
  • Qiang Yang
  • Yiqiang

We present a novel method for indoor-location estimation using a vector-space model based on signals received from a wireless client. Our aim is to obtain an accurate mapping between the signal space and the physical space without incurring too much human calibration effort. This problem has traditionally been tackled through probabilistic models trained on manually labeled data, which are expensive to obtain. In this paper, we present a novel approach to building a mapping between the signalvector space and the physical location space using kernel canonical correlation analysis (KCCA). Its training requires much less human labor. Moreover, unlike traditional location-estimation systems that treat grid points as independent and discrete target classes during training, we use the physical location as a continuous feedback to build a similarity mapping using KCCA. We test our algorithm in a 802. 11 wireless LAN environment, and demonstrate the advantage of our method in both accuracy and its ability to utilize a much smaller set of labeled training data than previous methods.

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AAAI Conference 2005 Conference Paper

Activity Recognition through Goal-Based Segmentation

  • Jie Yin
  • Qiang Yang

A major issue in activity recognition in a sensor network is how to automatically segment the low-level signal sequences in order to optimize the probabilistic recognition models for goals and activities. Past efforts have relied on segmenting the signal sequences by hand, which is both time-consuming and error-prone. In our view, segments should correspond to atomic human activities that enable a goal-recognizer to operate optimally; the two are intimately related. In this paper, we present a novel method for building probabilistic activity models at the same time as we segment signal sequences into motion patterns. We model each motion pattern as a linear dynamic model and the transitions between motion patterns as a Markov process conditioned on goals. Our EM learning algorithm simultaneously learns the motion-pattern boundaries and probabilistic models for goals and activities, which in turn can be used to accurately recognize activities in an online phase. A major advantage of our algorithm is that it can reduce the human effort in segmenting and labeling signal sequences. We demonstrate the effectiveness of our algorithm using the data collected in a real wireless environment.

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KER Journal 2005 Journal Article

Advances in conversational case-based reasoning

  • David W. Aha
  • David McSherry
  • Qiang Yang

A considerable amount of research in case-based reasoning (CBR) has recently focused on conversational CBR as a means of providing more effective support for interactive problem solving. We review progress made to date and identify challenges that remain to be addressed.

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IS Journal 2005 Journal Article

Guest Editors' Introduction: Data Mining in Bioinformatics

  • Jinyan Li
  • Limsoon Wong
  • Qiang Yang

This special issue aims to bridge the gap between bioinformatics and data mining by presenting research integrating the two. Data mining has the potential to provide the necessary tools for better understanding of gene expression, drug design, and other emerging problems in genomics and proteomics.

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IS Journal 2004 Journal Article

Guest Editors' Introduction: Mining Actionable Knowledge on the Web

  • Qiang Yang
  • C.A. Knoblock
  • Xindong Wu

The Web-its resources and users-offers a wealth of information for data mining and knowledge discovery. Up to now, a great deal of work has been done applying data mining and machine learning methods to discover novel and useful knowledge on the Web. However, many techniques aim only at extracting knowledge for human users to view and use. Recently, more and more work addresses Web for knowledge that computer systems will use. You can apply such actionable knowledge back to the Web for measurable performance improvements. This special issue of IEEE Intelligent Systems features five articles that address the problem of actionable Web mining.

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IJCAI Conference 1999 Conference Paper

Dynamic Refinement of Feature Weights Using Quantitative Introspective Learning

  • Zhong Zhang
  • Qiang Yang

Recently more and more researchers have been supporting the view that learning is a goaldriven process. One of the key properties of a goal-driven learner is introspectiveness - the ability to notice the gaps in its knowledge and to reason about the information required to fill in those gaps. In this paper, we introduce a quantitative introspective learning paradigm into case-based reasoning (CBR). The result is an integrated problem-solving model which will learn introspectively feature weights in a case base in order to be responsive dynamically to its users. In contrast to the existing qualitative methods for introspective learning, our model has the advantage of being able to capture accurate learning information in the interactions with its users. A CBR system equipped with quantitative introspective learning ability can allow the feature weights to be captured automatically and to track its users' changing preferences continuously. In such a system, while the reasoning part is still case-based, the learning part is shouldered by a quantitative introspective learning model. Weight learning and evolution are accomplished in the background. The effectiveness of this integration will be demonstrated through a series of empirical experiments.

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IJCAI Conference 1999 Conference Paper

Remembering to Add: Competence-preserving Case-Addition Policies for Case- Base Maintenance

  • Jun Zhu
  • Qiang Yang

Case-base maintenance is gaining increasing recognition in research and the practical applications of case-based reasoning (CBR). This intense interest is highlighted by Smyth and Keane's research on case deletion policies. In their work, Smyth and Keane advocated a case deletion policy, whereby the cases in a case base are classified and deleted based on their cover­ age potential and adaptation power. The al­ gorithm was empirically shown to improve the competence of a CBR system and outperform a number of previous deletion-based strategies. In this paper, we present a different case-base maintenance policy that is based on case addi­ tion rather than deletion. The advantage of our algorithm is that we can place a lower bound on the competence of the resulting case base; we demonstrate that the coverage of the com­ puted case base cannot be worse than the op­ timal case ba. se in coverage4 by a fixed lower bound, and the coverage is often much closer to optimum. We also show that the Smyth and Keane's deletion based policy cannot guarantee any such lower bound. Our result highlights the importance of finding the right ca. se-ba. se maintenance algorithm in order to guarantee the best case-base coverage. We demonstrate the effectiveness of our algorithm through an experiment in case-based planning.

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AAAI Conference 1990 Conference Paper

An Algebraic Approach to Conflict Resolution in Planning

  • Qiang Yang

This paper presents an algebra for conflict resolution in nonlinear planning. A set of conflicts in a plan is considered as a constraint network. Each node in the network represents a conflict, and is associated with a set of alternative ways for resolving it. Thus, resolving conflicts in a plan corresponds to selecting a set of consistent resolution methods so that, after they are applied to the plan, every conflict can be removed. The paper discusses the representional issues related to the conflict resolution, presents an algebra for resolving conflicts, and illustrates that some modified algorithms for preprocessing networks of constraints can greatly enhance the efficiency of conflict resolution.

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IJCAI Conference 1989 Conference Paper

Preprocessing Search Spaces for Branch and Bound Search

  • Qiang Yang
  • Dana 5. Nau

Heuristic search procedures are useful in a large number of problems of practical importance. Such procedures operate by searching several paths in a search space at the same time, expanding some paths more quickly than others depending on which paths look most promising. Often large amounts of time are required in keeping track of the control knowledge. For some problems, this overhead can be greatly reduced by preprocessing the problem in appropriate ways. In particular, we discuss a data structure called a threaded decision graph, which can be created by preprocessing the search space for some problems, and winch captures the control knowledge for prob lern solving We show how this can be done, and we present an analysis showing that by us ing such a method, a great deal of time can be saved during problem solving processes.

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