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Yong Yu

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EAAI Journal 2026 Journal Article

A smart computational framework for predicting mechanical and sustainability indicators and optimizing mix proportions of recycled rubber aggregate concrete

  • Lang Lin
  • Nuo Xu
  • Di Yang
  • Guangzhou Li
  • Yiming Xiao
  • Yong Yu

Recycled rubber aggregate concrete (RRAC), a sustainable composite in which end-of-life tire rubber replaces natural aggregates, aids waste reduction, conserves resources, lowers structural weight and enhances acoustic and energy-dissipation performance. Yet its wider deployment remains constrained by limited accuracy in predicting mechanical properties and by the inefficiency of conventional mix-design practices. This study thus introduces an integrated framework that unifies property prediction, sustainability assessment and mix-design optimization. A dataset of 1382 experiments was used to train compressive strength (f c) and elastic modulus (E) models using random forest, gradient-boosted regression trees, extreme gradient boosting (XGB), light gradient boosting machine and a Bayesian neural network, from which the top-performing model was identified. Model transparency was achieved through Shapley additive explanations, partial dependence plots and individual conditional expectation analysis. Life-cycle carbon emissions of RRAC were quantified, and particle swarm optimization was employed to balance f c, E and carbon footprint, yielding optimized mixture formulations. Key findings include: (a) Predictive models attained R 2 values of 0. 584–0. 759 for f c and 0. 674–0. 842 for E, with train-test gaps ≤0. 05, demonstrating solid accuracy and generalization, with XGB performing best. (b) Feature-importance analysis showed that f c was governed primarily by recycled fine-aggregate substitution, water-to-cement ratio, recycled coarse aggregate substitution, sand ratio and aggregate-to-cement ratio, with E following a similar hierarchy. (c) Particle swarm optimization produced mix designs that reconcile strength, stiffness and emissions. Relative to unoptimized mixtures, optimized RRAC lowered carbon emissions by 20 %–55 % without sacrificing mechanical performance, offering a robust pathway toward sustainable concrete design.

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AAMAS Conference 2026 Conference Paper

Automatically Benchmarking LLM Code Agents through Agent-driven Annotation and Evaluation

  • Lingyue Fu
  • Bolun Zhang
  • Hao Guan
  • Yaoming Zhu
  • Lin Qiu
  • Weiwen Liu
  • Xuezhi Cao
  • Xunliang Cai

Recent advances in code agents have enabled automated software development at the project level, supported by large language models(LLMs)andwidelyadoptedtools. However, existingbenchmarks for code agent evaluation face two major limitations: high annotation cost and expertise requirements, and rigid evaluation metrics thatrelyprimarilyonunittests. Toaddressthesechallenges, wepropose an agent-driven benchmark construction pipeline that leverages human supervision to efficiently generate diverse and challenging project-level tasks. Based on this approach, we introduce PRDBench, a novel benchmark comprising 50 real-world Python projects across 20 domains, each with structured Product Requirement Document (PRD) requirements, comprehensive evaluation criteria, and reference implementations. PRDBench features rich data sources, high task complexity, and flexible metrics. We further employ an Agent-as-a-Judge paradigm to score agent outputs, enabling the evaluation of various test types beyond unit tests. Extensive experiments on PRDBench demonstrate its effectiveness in assessing the capabilities of both code agents and evaluation agents, providing a scalable and robust framework for annotation and evaluation.

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AAMAS Conference 2026 Conference Paper

DebugTA: An LLM-Based Agent for Simplifying Debugging and Teaching in Programming Education

  • Lingyue Fu
  • Datong Chen
  • Haowei Yuan
  • Xinyi Dai
  • Qingyao Li
  • Weinan Zhang
  • Weiwen Liu
  • Yong Yu

In programming education, Debugging and Teaching (DT) task is a common scenario which requires generating modification suggestions from erroneous code, error messages, reference solutions, and problem descriptions. Existing approaches struggle with complex multi-source reasoning and underutilize available reference code, limiting the effectiveness of large language models (LLMs) in DT tasks. To address these challenges, we propose DebugTA, a novel LLM-based debugging and teaching agent with specialized tools for standard code retrieval, variable substitution to align reference code, and an external compiler for real-time code analysis. Guided by pedagogical and debugging principles, DebugTA decomposes complexDTtasksintostructuredLLM–toolinteractionsthatreduce reasoning complexity. By aligning reference code with erroneous code, DebugTA enables the LLM to focus on logical errors and improves suggestion accuracy. To rigorously assess the quality of modification suggestions, we introduce a student simulator-teacher interactionparadigm. Experimentalresultsonthreereal-worldcode datasets demonstrate that DebugTA consistently improves teaching effectiveness while significantly reducing computational costs.

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AAMAS Conference 2026 Conference Paper

Learning Semantic and Structure Aware Representation with Large Language Models for Concept Recommendation

  • Qingyao Li
  • Wei Xia
  • Kounianhua Du
  • Qiji Zhang
  • Weinan Zhang
  • Ruiming Tang
  • Yong Yu

Concept recommendation aims to suggest the next concept aligned with both the learner’s state and the educational knowledge system. However, existing methods often overlook concept semantics, leadingtorecommendationsthatlacksemanticrelevanceandstructural consistency. To address this, we propose SSRec, a novel Semantic and Structure aware representation learning framework. SSRec leverages Large Language Models (LLMs) to capture concept semanticsandintroducesagraph-basedadapter. Thisadapternotonly integrates structural relationships but also transforms anisotropic text encodings into a smooth representation space. Extensive experiments on real-world datasets demonstrate that SSRec significantly outperforms state-of-the-art baselines in delivering accurate and consistent recommendations.

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AAAI Conference 2026 Conference Paper

Offline Fictitious Self-Play for Competitive Games

  • Jingxiao Chen
  • Weiji Xie
  • Weinan Zhang
  • Yong Yu
  • Ying Wen

Offline Reinforcement Learning (RL) enables policy improvement from fixed datasets without online interactions, making it highly suitable for real-world applications lacking efficient simulators. Despite its success in the single-agent setting, offline multi-agent RL remains a challenge, especially in competitive games. Firstly, unaware of the game structure, it is impossible to interact with the opponents and conduct a major learning paradigm, self-play, for competitive games. Secondly, real-world datasets cannot cover all the state and action space in the game, resulting in barriers to identifying Nash equilibrium (NE). To address these issues, this paper introduces Off-FSP, the first practical model-free offline RL algorithm for competitive games. We start by simulating interactions with various opponents by adjusting the weights of the fixed dataset with importance sampling. This technique allows us to learn the best responses to different opponents and employ the Offline Self-Play learning framework. To overcome the challenge of partial coverage, we combine the single-agent offline RL method with Fictitious Self-Play (FSP) to approximate NE by constraining the approximate best responses away from out-of-distribution actions. Experiments on matrix games, extensive-form poker, and board games demonstrate that Off-FSP achieves significantly lower exploitability than state-of-the-art baselines. Finally, we validate Off-FSP on a real-world human-robot competitive task, demonstrating its potential for solving complex, hard-to-simulate real-world problems.

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TCS Journal 2025 Journal Article

A continuous leakage-amplified IBE scheme with perfect key update

  • Zirui Qiao
  • Yong Yu
  • Yanwei Zhou
  • Dong Zheng

In the practical deployment of cryptographic solutions, diverse applications pose unique challenges in terms of leakage resilience. The one-size-fits-all approach of traditional cryptographic primitives and fixed leakage-resilient ability often fails to satisfy the nuanced security demands of different scenarios. Recognizing this, the concept of a continuous Identity-based Encryption (IBE) scheme has been introduced. This innovative approach allows for the dynamic adjustment of private key lengths to tailor the system’s resistance to various leakage attacks, based on the specific needs of an application. Despite the strides made, the existing implementations of this scheme exhibit limitations, particularly in the aspect of key updates. The current method for key updates is incomplete, only updating some elements of the key. This process relies on a trapdoor mechanism, which results in suboptimal storage efficiency. This paper introduces a new continuous leakage amplified IBE scheme to address these shortcomings. This improved model features a comprehensive key update mechanism that enables users to refresh every element of the private key without utilizing a trapdoor. Our analysis confirms that this enhanced IBE scheme stands out for its security, efficiency, and practicality. Moreover, in pursuit of optimizing performance, we present a novel general construction. This construction illustrates that it is feasible to construct an IBE scheme resilient against chosen-ciphertext attacks, equipped with an impeccable update function, by building upon any semantically secure IBE scheme.

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EAAI Journal 2025 Journal Article

A hybrid Bayesian model updating and non-dominated sorting genetic algorithm framework for intelligent mix design of steel fiber reinforced concrete

  • Yong Yu
  • Jie Su
  • Bo Wu

Steel fiber reinforced concrete (SFRC) improves the strength and toughness of conventional concrete, but the high cost and carbon footprint of fibers challenge the balance among performance, cost and sustainability. To address this, an intelligent mix design framework is proposed to optimize compressive and splitting tensile strengths, cost and emissions. Based on 671 experimental records, posterior models were built using Markov Chain Monte Carlo sampling and Bayesian model updating, enabling accurate strength predictions. Compared to traditional regression methods, R 2 scores improved by 15. 7 % and 12. 4 %, confirming its predictive advantage. Cost-wise, materials dominate, while emissions mainly arise from production, transport and mixing. A non-dominated sorting genetic algorithm identified optimal designs under given constraints. Results show that reducing water-to-cement and aggregate-to-cement ratios, and increasing sand ratio and fiber reinforcement index, enhances SFRC strength. Larger coarse aggregates reduce compressive strength but have limited effect on tensile strength. Optimization suggests potential cost and emission reductions of up to 60 %. Moreover, for compression-prone components, fiber use is inefficient due to high cost and emissions, whereas for crack-resistant or strength-balanced elements, fiber inclusion offers a more sustainable alternative to merely lowering the water-to-cement ratio. The proposed framework enables tailored SFRC mix designs, guiding the efficient use of steel fibers.

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AAAI Conference 2025 Conference Paper

Beyond Graph Convolution: Multimodal Recommendation with Topology-aware MLPs

  • Junjie Huang
  • Jiarui Qin
  • Yong Yu
  • Weinan Zhang

Given the large volume of side information from different modalities, multimodal recommender systems have become increasingly vital, as they exploit richer semantic information beyond user-item interactions. Recent works highlight that leveraging Graph Convolutional Networks (GCNs) to explicitly model multimodal item-item relations can significantly enhance recommendation performance. However, due to the inherent over-smoothing issue of GCNs, existing models benefit only from shallow GCNs with limited representation power. This drawback is especially pronounced when facing complex and high-dimensional patterns such as multimodal data, as it requires large-capacity models to accommodate complicated correlations. To this end, in this paper, we investigate bypassing GCNs when modeling multimodal item-item relationship. More specifically, we propose a Topology-aware Multi-Layer Perceptron (TMLP), which uses MLPs instead of GCNs to model the relationships between items. TMLP enhances MLPs with topological pruning to denoise item-item relations and intra (inter)-modality learning to integrate higher-order modality correlations. Extensive experiments on three real-world datasets verify TMLP's superiority over nine baselines. We also find that by discarding the internal message passing in GCNs, which is sensitive to node connections, TMLP achieves significant improvements in both training efficiency and robustness against existing models.

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NeurIPS Conference 2025 Conference Paper

Stop DDoS Attacking the Research Community with AI-Generated Survey Papers

  • Jianghao Lin
  • Rong Shan
  • Jiachen Zhu
  • Yunjia Xi
  • Yong Yu
  • Weinan Zhang

Survey papers are foundational to the scholarly progress of research communities, offering structured overviews that guide both novices and experts across disciplines. However, the recent surge of AI-generated surveys, especially enabled by large language models (LLMs), has transformed this traditionally labor-intensive genre into a low-effort, high-volume output. While such automation lowers entry barriers, it also introduces a critical threat: the phenomenon we term the "survey paper DDoS attack" to the research community. This refers to the unchecked proliferation of superficially comprehensive but often redundant, low-quality, or even hallucinated survey manuscripts, which floods preprint platforms, overwhelms researchers, and erodes trust in the scientific record. In this position paper, we argue that we must stop uploading massive amounts of AI-generated survey papers (i. e. , survey paper DDoS attack) to the research community, by instituting strong norms for AI-assisted review writing. We call for restoring expert oversight and transparency in AI usage and, moreover, developing new infrastructures such as Dynamic Live Surveys, community-maintained, version-controlled repositories that blend automated updates with human curation. Through quantitative trend analysis, quality audits, and cultural impact discussion, we show that safeguarding the integrity of surveys is no longer optional but imperative to the research community.

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NeurIPS Conference 2024 Conference Paper

MADiff: Offline Multi-agent Learning with Diffusion Models

  • Zhengbang Zhu
  • Minghuan Liu
  • Liyuan Mao
  • Bingyi Kang
  • Minkai Xu
  • Yong Yu
  • Stefano Ermon
  • Weinan Zhang

Offline reinforcement learning (RL) aims to learn policies from pre-existing datasets without further interactions, making it a challenging task. Q-learning algorithms struggle with extrapolation errors in offline settings, while supervised learning methods are constrained by model expressiveness. Recently, diffusion models (DMs) have shown promise in overcoming these limitations in single-agent learning, but their application in multi-agent scenarios remains unclear. Generating trajectories for each agent with independent DMs may impede coordination, while concatenating all agents’ information can lead to low sample efficiency. Accordingly, we propose MADiff, which is realized with an attention-based diffusion model to model the complex coordination among behaviors of multiple agents. To our knowledge, MADiff is the first diffusion-based multi-agent learning framework, functioning as both a decentralized policy and a centralized controller. During decentralized executions, MADiff simultaneously performs teammate modeling, and the centralized controller can also be applied in multi-agent trajectory predictions. Our experiments demonstrate that MADiff outperforms baseline algorithms across various multi-agent learning tasks, highlighting its effectiveness in modeling complex multi-agent interactions.

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JMLR Journal 2023 Journal Article

Adaptation Augmented Model-based Policy Optimization

  • Jian Shen
  • Hang Lai
  • Minghuan Liu
  • Han Zhao
  • Yong Yu
  • Weinan Zhang

Compared to model-free reinforcement learning (RL), model-based RL is often more sample efficient by leveraging a learned dynamics model to help decision making. However, the learned model is usually not perfectly accurate and the error will compound in multi-step predictions, which can lead to poor asymptotic performance. In this paper, we first derive an upper bound of the return discrepancy between the real dynamics and the learned model, which reveals the fundamental problem of distribution shift between simulated data and real data. Inspired by the theoretical analysis, we propose an adaptation augmented model-based policy optimization (AMPO) framework to address the distribution shift problem from the perspectives of feature learning and instance re-weighting, respectively. Specifically, the feature-based variant, namely FAMPO, introduces unsupervised model adaptation to minimize the integral probability metric (IPM) between feature distributions from real and simulated data, while the instance-based variant, termed as IAMPO, utilizes importance sampling to re-weight the real samples used to train the model. Besides model learning, we also investigate how to improve policy optimization in the model usage phase by selecting simulated samples with different probability according to their uncertainty. Extensive experiments on challenging continuous control tasks show that FAMPO and IAMPO, coupled with our model usage technique, achieves superior performance against baselines, which demonstrates the effectiveness of the proposed methods. [abs] [ pdf ][ bib ] &copy JMLR 2023. ( edit, beta )

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AAAI Conference 2023 Conference Paper

Learning Decomposed Spatial Relations for Multi-Variate Time-Series Modeling

  • Yuchen Fang
  • Kan Ren
  • Caihua Shan
  • Yifei Shen
  • You Li
  • Weinan Zhang
  • Yong Yu
  • Dongsheng Li

Modeling multi-variate time-series (MVTS) data is a long-standing research subject and has found wide applications. Recently, there is a surge of interest in modeling spatial relations between variables as graphs, i.e., first learning one static graph for each dataset and then exploiting the graph structure via graph neural networks. However, as spatial relations may differ substantially across samples, building one static graph for all the samples inherently limits flexibility and severely degrades the performance in practice. To address this issue, we propose a framework for fine-grained modeling and utilization of spatial correlation between variables. By analyzing the statistical properties of real-world datasets, a universal decomposition of spatial correlation graphs is first identified. Specifically, the hidden spatial relations can be decomposed into a prior part, which applies across all the samples, and a dynamic part, which varies between samples, and building different graphs is necessary to model these relations. To better coordinate the learning of the two relational graphs, we propose a min-max learning paradigm that not only regulates the common part of different dynamic graphs but also guarantees spatial distinguishability among samples. The experimental results show that our proposed model outperforms the state-of-the-art baseline methods on both time-series forecasting and time-series point prediction tasks.

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NeurIPS Conference 2023 Conference Paper

Lending Interaction Wings to Recommender Systems with Conversational Agents

  • Jiarui Jin
  • Xianyu Chen
  • Fanghua Ye
  • Mengyue Yang
  • Yue Feng
  • Weinan Zhang
  • Yong Yu
  • Jun Wang

An intelligent conversational agent (a. k. a. , chat-bot) could embrace conversational technologies to obtain user preferences online, to overcome inherent limitations of recommender systems trained over the offline historical user behaviors. In this paper, we propose CORE, a new offline-training and online-checking framework to plug a COnversational agent into REcommender systems. Unlike most prior conversational recommendation approaches that systemically combine conversational and recommender parts through a reinforcement learning framework, CORE bridges the conversational agent and recommender system through a unified uncertainty minimization framework, which can be easily applied to any existing recommendation approach. Concretely, CORE treats a recommender system as an offline estimator to produce an estimated relevance score for each item, while CORE regards a conversational agent as an online checker that checks these estimated scores in each online session. We define uncertainty as the sum of unchecked relevance scores. In this regard, the conversational agent acts to minimize uncertainty via querying either attributes or items. Towards uncertainty minimization, we derive the certainty gain of querying each attribute and item, and develop a novel online decision tree algorithm to decide what to query at each turn. Our theoretical analysis reveals the bound of the expected number of turns of CORE in a cold-start setting. Experimental results demonstrate that CORE can be seamlessly employed on a variety of recommendation approaches, and can consistently bring significant improvements in both hot-start and cold-start settings.

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JMLR Journal 2023 Journal Article

MALib: A Parallel Framework for Population-based Multi-agent Reinforcement Learning

  • Ming Zhou
  • Ziyu Wan
  • Hanjing Wang
  • Muning Wen
  • Runzhe Wu
  • Ying Wen
  • Yaodong Yang
  • Yong Yu

Population-based multi-agent reinforcement learning (PB-MARL) encompasses a range of methods that merge dynamic population selection with multi-agent reinforcement learning algorithms (MARL). While PB-MARL has demonstrated notable achievements in complex multi-agent tasks, its sequential execution is plagued by low computational efficiency due to the diversity in computing patterns and policy combinations. We propose a solution involving a stateless central task dispatcher and stateful workers to handle PB-MARL's subroutines, thereby capitalizing on parallelism across various components for efficient problem-solving. In line with this approach, we introduce MALib, a parallel framework that incorporates a task control model, independent data servers, and an abstraction of MARL training paradigms. The framework has undergone extensive testing and is available under the MIT license (https://github.com/sjtu-marl/malib) [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2023. ( edit, beta )

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AAAI Conference 2023 Conference Paper

Set-to-Sequence Ranking-Based Concept-Aware Learning Path Recommendation

  • Xianyu Chen
  • Jian Shen
  • Wei Xia
  • Jiarui Jin
  • Yakun Song
  • Weinan Zhang
  • Weiwen Liu
  • Menghui Zhu

With the development of the online education system, personalized education recommendation has played an essential role. In this paper, we focus on developing path recommendation systems that aim to generating and recommending an entire learning path to the given user in each session. Noticing that existing approaches fail to consider the correlations of concepts in the path, we propose a novel framework named Set-to-Sequence Ranking-based Concept-aware Learning Path Recommendation (SRC), which formulates the recommendation task under a set-to-sequence paradigm. Specifically, we first design a concept-aware encoder module which can capture the correlations among the input learning concepts. The outputs are then fed into a decoder module that sequentially generates a path through an attention mechanism that handles correlations between the learning and target concepts. Our recommendation policy is optimized by policy gradient. In addition, we also introduce an auxiliary module based on knowledge tracing to enhance the model’s stability by evaluating students’ learning effects on learning concepts. We conduct extensive experiments on two real-world public datasets and one industrial dataset, and the experimental results demonstrate the superiority and effectiveness of SRC. Code now is available at https://gitee.com/mindspore/models/tree/master/research/recommend/SRC.

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NeurIPS Conference 2022 Conference Paper

Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement Learning

  • Hua Wei
  • Jingxiao Chen
  • Xiyang Ji
  • Hongyang Qin
  • Minwen Deng
  • Siqin Li
  • Liang Wang
  • Weinan Zhang

This paper introduces Honor of Kings Arena, a reinforcement learning (RL) environment based on the Honor of Kings, one of the world’s most popular games at present. Compared to other environments studied in most previous work, ours presents new generalization challenges for competitive reinforcement learning. It is a multi-agent problem with one agent competing against its opponent; and it requires the generalization ability as it has diverse targets to control and diverse opponents to compete with. We describe the observation, action, and reward specifications for the Honor of Kings domain and provide an open-source Python-based interface for communicating with the game engine. We provide twenty target heroes with a variety of tasks in Honor of Kings Arena and present initial baseline results for RL-based methods with feasible computing resources. Finally, we showcase the generalization challenges imposed by Honor of Kings Arena and possible remedies to the challenges. All of the software, including the environment-class, are publicly available.

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AAAI Conference 2022 Conference Paper

Multi-View Graph Representation for Programming Language Processing: An Investigation into Algorithm Detection

  • Ting Long
  • Yutong Xie
  • Xianyu Chen
  • Weinan Zhang
  • Qinxiang Cao
  • Yong Yu

Program representation, which aims at converting program source code into vectors with automatically extracted features, is a fundamental problem in programming language processing (PLP). Recent work tries to represent programs with neural networks based on source code structures. However, such methods often focus on the syntax and consider only one single perspective of programs, limiting the representation power of models. This paper proposes a multiview graph (MVG) program representation method. MVG pays more attention to code semantics and simultaneously includes both data flow and control flow as multiple views. These views are then combined and processed by a graph neural network (GNN) to obtain a comprehensive program representation that covers various aspects. We thoroughly evaluate our proposed MVG approach in the context of algorithm detection, an important and challenging subfield of PLP. Specifically, we use a public dataset POJ-104 and also construct a new challenging dataset ALG-109 to test our method. In experiments, MVG outperforms previous methods significantly, demonstrating our model’s strong capability of representing source code.

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IJCAI Conference 2021 Conference Paper

MapGo: Model-Assisted Policy Optimization for Goal-Oriented Tasks

  • Menghui Zhu
  • Minghuan Liu
  • Jian Shen
  • Zhicheng Zhang
  • Sheng Chen
  • Weinan Zhang
  • Deheng Ye
  • Yong Yu

In Goal-oriented Reinforcement learning, relabeling the raw goals in past experience to provide agents with hindsight ability is a major solution to the reward sparsity problem. In this paper, to enhance the diversity of relabeled goals, we develop FGI (Foresight Goal Inference), a new relabeling strategy that relabels the goals by looking into the future with a learned dynamics model. Besides, to improve sample efficiency, we propose to use the dynamics model to generate simulated trajectories for policy training. By integrating these two improvements, we introduce the MapGo framework (Model-Assisted Policy optimization for Goal-oriented tasks). In our experiments, we first show the effectiveness of the FGI strategy compared with the hindsight one, and then show that the MapGo framework achieves higher sample efficiency when compared to model-free baselines on a set of complicated tasks.

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NeurIPS Conference 2021 Conference Paper

On Effective Scheduling of Model-based Reinforcement Learning

  • Hang Lai
  • Jian Shen
  • Weinan Zhang
  • Yimin Huang
  • Xing Zhang
  • Ruiming Tang
  • Yong Yu
  • Zhenguo Li

Model-based reinforcement learning has attracted wide attention due to its superior sample efficiency. Despite its impressive success so far, it is still unclear how to appropriately schedule the important hyperparameters to achieve adequate performance, such as the real data ratio for policy optimization in Dyna-style model-based algorithms. In this paper, we first theoretically analyze the role of real data in policy training, which suggests that gradually increasing the ratio of real data yields better performance. Inspired by the analysis, we propose a framework named AutoMBPO to automatically schedule the real data ratio as well as other hyperparameters in training model-based policy optimization (MBPO) algorithm, a representative running case of model-based methods. On several continuous control tasks, the MBPO instance trained with hyperparameters scheduled by AutoMBPO can significantly surpass the original one, and the real data ratio schedule found by AutoMBPO shows consistency with our theoretical analysis.

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AAAI Conference 2021 Conference Paper

Universal Trading for Order Execution with Oracle Policy Distillation

  • Yuchen Fang
  • Kan Ren
  • Weiqing Liu
  • Dong Zhou
  • Weinan Zhang
  • Jiang Bian
  • Yong Yu
  • Tie-Yan Liu

As a fundamental problem in algorithmic trading, order execution aims at fulfilling a specific trading order, either liquidation or acquirement, for a given instrument. Towards effective execution strategy, recent years have witnessed the shift from the analytical view with model-based market assumptions to model-free perspective, i. e. , reinforcement learning, due to its nature of sequential decision optimization. However, the noisy and yet imperfect market information that can be leveraged by the policy has made it quite challenging to build up sample efficient reinforcement learning methods to achieve effective order execution. In this paper, we propose a novel universal trading policy optimization framework to bridge the gap between the noisy yet imperfect market states and the optimal action sequences for order execution. Particularly, this framework leverages a policy distillation method that can better guide the learning of the common policy towards practically optimal execution by an oracle teacher with perfect information to approximate the optimal trading strategy. The extensive experiments have shown significant improvements of our method over various strong baselines, with reasonable trading actions.

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IJCAI Conference 2020 Conference Paper

Aggregating Crowd Wisdom with Side Information via a Clustering-based Label-aware Autoencoder

  • Li'ang Yin
  • Yunfei Liu
  • Weinan Zhang
  • Yong Yu

Aggregating crowd wisdom infers true labels for objects, from multiple noisy labels provided by various sources. Besides labels from sources, side information such as object features is also introduced to achieve higher inference accuracy. Usually, the learning-from-crowds framework is adopted. However, the framework considers each object in isolation and does not make full use of object features to overcome label noise. In this paper, we propose a clustering-based label-aware autoencoder (CLA) to alleviate label noise. CLA utilizes clusters to gather objects with similar features and exploits clustering to infer true labels, by constructing a novel deep generative process to simultaneously generate object features and source labels from clusters. For model inference, CLA extends the framework of variational autoencoders and utilizes maximizing a posteriori (MAP) estimation, which prevents the model from overfitting and trivial solutions. Experiments on real-world tasks demonstrate the significant improvement of CLA compared with the state-of-the-art aggregation algorithms.

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IJCAI Conference 2020 Conference Paper

DropNAS: Grouped Operation Dropout for Differentiable Architecture Search

  • Weijun Hong
  • Guilin Li
  • Weinan Zhang
  • Ruiming Tang
  • Yunhe Wang
  • Zhenguo Li
  • Yong Yu

Neural architecture search (NAS) has shown encouraging results in automating the architecture design. Recently, DARTS relaxes the search process with a differentiable formulation that leverages weight-sharing and SGD for cost reduction of NAS. In DARTS, all candidate operations are trained simultaneously during the network weight training step. Our empirical results show that this training procedure leads to the co-adaption problem and Matthew Effect: operations with fewer parameters would be trained maturely earlier. This causes two problems: firstly, the operations with more parameters may never have the chance to express the desired function since those with less have already done the job; secondly, the system will punish those underperforming operations by lowering their architecture parameter and backward smaller loss gradients, this causes the Matthew Effect. In this paper, we systematically study these problems and propose a novel grouped operation dropout algorithm named DropNAS to fix the problems with DARTS. Extensive experiments demonstrate that DropNAS solves the above issues and achieves promising performance. Specifically, DropNAS achieves 2. 26% test error on CIFAR-10, 16. 39% on CIFAR-100 and 23. 4% on ImageNet (with the same training hyperparameters as DARTS for a fair comparison). It is also observed that DropNAS is robust across variants of the DARTS search space. Code is available at https: //github. com/huawei-noah.

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IJCAI Conference 2020 Conference Paper

Efficient and Robust High-Dimensional Linear Contextual Bandits

  • Cheng Chen
  • Luo Luo
  • Weinan Zhang
  • Yong Yu
  • Yijiang Lian

The linear contextual bandits is a sequential decision-making problem where an agent decides among sequential actions given their corresponding contexts. Since large-scale data sets become more and more common, we study the linear contextual bandits in high-dimensional situations. Recent works focus on employing matrix sketching methods to accelerating contextual bandits. However, the matrix approximation error will bring additional terms to the regret bound. In this paper we first propose a novel matrix sketching method which is called Spectral Compensation Frequent Directions (SCFD). Then we propose an efficient approach for contextual bandits by adopting SCFD to approximate the covariance matrices. By maintaining and manipulating sketched matrices, our method only needs O(md) space and O(md) updating time in each round, where d is the dimensionality of the data and m is the sketching size. Theoretical analysis reveals that our method has better regret bounds than previous methods in high-dimensional cases. Experimental results demonstrate the effectiveness of our algorithm and verify our theoretical guarantees.

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NeurIPS Conference 2020 Conference Paper

Efficient Projection-free Algorithms for Saddle Point Problems

  • Cheng Chen
  • Luo Luo
  • Weinan Zhang
  • Yong Yu

The Frank-Wolfe algorithm is a classic method for constrained optimization problems. It has recently been popular in many machine learning applications because its projection-free property leads to more efficient iterations. In this paper, we study projection-free algorithms for convex-strongly-concave saddle point problems with complicated constraints. Our method combines Conditional Gradient Sliding with Mirror-Prox and show that it only requires $\tilde{\cO}(1/\sqrt{\epsilon})$ gradient evaluations and $\tilde{\cO}(1/\epsilon^2)$ linear optimizations in the batch setting. We also extend our method to the stochastic setting and propose first stochastic projection-free algorithms for saddle point problems. Experimental results demonstrate the effectiveness of our algorithms and verify our theoretical guarantees.

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IJCAI Conference 2020 Conference Paper

Improving Knowledge Tracing via Pre-training Question Embeddings

  • Yunfei Liu
  • Yang Yang
  • Xianyu Chen
  • Jian Shen
  • Haifeng Zhang
  • Yong Yu

Knowledge tracing (KT) defines the task of predicting whether students can correctly answer questions based on their historical response. Although much research has been devoted to exploiting the question information, plentiful advanced information among questions and skills hasn't been well extracted, making it challenging for previous work to perform adequately. In this paper, we demonstrate that large gains on KT can be realized by pre-training embeddings for each question on abundant side information, followed by training deep KT models on the obtained embeddings. To be specific, the side information includes question difficulty and three kinds of relations contained in a bipartite graph between questions and skills. To pre-train the question embeddings, we propose to use product-based neural networks to recover the side information. As a result, adopting the pre-trained embeddings in existing deep KT models significantly outperforms state-of-the-art baselines on three common KT datasets.

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AAAI Conference 2020 Conference Paper

Infomax Neural Joint Source-Channel Coding via Adversarial Bit Flip

  • Yuxuan Song
  • Minkai Xu
  • Lantao Yu
  • Hao Zhou
  • Shuo Shao
  • Yong Yu

Although Shannon theory states that it is asymptotically optimal to separate the source and channel coding as two independent processes, in many practical communication scenarios this decomposition is limited by the finite bit-length and computational power for decoding. Recently, neural joint source-channel coding (NECST) (Choi et al. 2018) is proposed to sidestep this problem. While it leverages the advancements of amortized inference and deep learning (Kingma and Welling 2013; Grover and Ermon 2018) to improve the encoding and decoding process, it still cannot always achieve compelling results in terms of compression and error correction performance due to the limited robustness of its learned coding networks. In this paper, motivated by the inherent connections between neural joint source-channel coding and discrete representation learning, we propose a novel regularization method called Infomax Adversarial-Bit- Flip (IABF) to improve the stability and robustness of the neural joint source-channel coding scheme. More specifically, on the encoder side, we propose to explicitly maximize the mutual information between the codeword and data; while on the decoder side, the amortized reconstruction is regularized within an adversarial framework. Extensive experiments conducted on various real-world datasets evidence that our IABF can achieve state-of-the-art performances on both compression and error correction benchmarks and outperform the baselines by a significant margin.

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NeurIPS Conference 2020 Conference Paper

Model-based Policy Optimization with Unsupervised Model Adaptation

  • Jian Shen
  • Han Zhao
  • Weinan Zhang
  • Yong Yu

Model-based reinforcement learning methods learn a dynamics model with real data sampled from the environment and leverage it to generate simulated data to derive an agent. However, due to the potential distribution mismatch between simulated data and real data, this could lead to degraded performance. Despite much effort being devoted to reducing this distribution mismatch, existing methods fail to solve it explicitly. In this paper, we investigate how to bridge the gap between real and simulated data due to inaccurate model estimation for better policy optimization. To begin with, we first derive a lower bound of the expected return, which naturally inspires a bound maximization algorithm by aligning the simulated and real data distributions. To this end, we propose a novel model-based reinforcement learning framework AMPO, which introduces unsupervised model adaptation to minimize the integral probability metric (IPM) between feature distributions from real and simulated data. Instantiating our framework with Wasserstein-1 distance gives a practical model-based approach. Empirically, our approach achieves state-of-the-art performance in terms of sample efficiency on a range of continuous control benchmark tasks.

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AAAI Conference 2020 Conference Paper

Towards Making the Most of BERT in Neural Machine Translation

  • Jiacheng Yang
  • Mingxuan Wang
  • Hao Zhou
  • Chengqi Zhao
  • Weinan Zhang
  • Yong Yu
  • Lei Li

GPT-2 and BERT demonstrate the effectiveness of using pretrained language models (LMs) on various natural language processing tasks. However, LM fine-tuning often suffers from catastrophic forgetting when applied to resource-rich tasks. In this work, we introduce a concerted training framework (CTNMT) that is the key to integrate the pre-trained LMs to neural machine translation (NMT). Our proposed CTNMT consists of three techniques: a) asymptotic distillation to ensure that the NMT model can retain the previous pre-trained knowledge; b) a dynamic switching gate to avoid catastrophic forgetting of pre-trained knowledge; and c) a strategy to adjust the learning paces according to a scheduled policy. Our experiments in machine translation show CTNMT gains of up to 3 BLEU score on the WMT14 English-German language pair which even surpasses the previous state-of-the-art pretraining aided NMT by 1. 4 BLEU score. While for the large WMT14 English-French task with 40 millions of sentencepairs, our base model still significantly improves upon the state-of-the-art Transformer big model by more than 1 BLEU score.

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AAAI Conference 2019 Conference Paper

Deep Recurrent Survival Analysis

  • Kan Ren
  • Jiarui Qin
  • Lei Zheng
  • Zhengyu Yang
  • Weinan Zhang
  • Lin Qiu
  • Yong Yu

Survival analysis is a hotspot in statistical research for modeling time-to-event information with data censorship handling, which has been widely used in many applications such as clinical research, information system and other fields with survivorship bias. Many works have been proposed for survival analysis ranging from traditional statistic methods to machine learning models. However, the existing methodologies either utilize counting-based statistics on the segmented data, or have a pre-assumption on the event probability distribution w. r. t. time. Moreover, few works consider sequential patterns within the feature space. In this paper, we propose a Deep Recurrent Survival Analysis model which combines deep learning for conditional probability prediction at finegrained level of the data, and survival analysis for tackling the censorship. By capturing the time dependency through modeling the conditional probability of the event for each sample, our method predicts the likelihood of the true event occurrence and estimates the survival rate over time, i. e. , the probability of the non-occurrence of the event, for the censored data. Meanwhile, without assuming any specific form of the event probability distribution, our model shows great advantages over the previous works on fitting various sophisticated data distributions. In the experiments on the three realworld tasks from different fields, our model significantly outperforms the state-of-the-art solutions under various metrics.

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AAAI Conference 2019 Conference Paper

Guiding the One-to-One Mapping in CycleGAN via Optimal Transport

  • Guansong Lu
  • Zhiming Zhou
  • Yuxuan Song
  • Kan Ren
  • Yong Yu

CycleGAN is capable of learning a one-to-one mapping between two data distributions without paired examples, achieving the task of unsupervised data translation. However, there is no theoretical guarantee on the property of the learned one-to-one mapping in CycleGAN. In this paper, we experimentally find that, under some circumstances, the one-to-one mapping learned by CycleGAN is just a random one within the large feasible solution space. Based on this observation, we explore to add extra constraints such that the one-to-one mapping is controllable and satisfies more properties related to specific tasks. We propose to solve an optimal transport mapping restrained by a task-specific cost function that reflects the desired properties, and use the barycenters of optimal transport mapping to serve as references for CycleGAN. Our experiments indicate that the proposed algorithm is capable of learning a one-to-one mapping with the desired properties.

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IJCAI Conference 2019 Conference Paper

Hybrid Actor-Critic Reinforcement Learning in Parameterized Action Space

  • Zhou Fan
  • Rui Su
  • Weinan Zhang
  • Yong Yu

In this paper we propose a hybrid architecture of actor-critic algorithms for reinforcement learning in parameterized action space, which consists of multiple parallel sub-actor networks to decompose the structured action space into simpler action spaces along with a critic network to guide the training of all sub-actor networks. While this paper is mainly focused on parameterized action space, the proposed architecture, which we call hybrid actor-critic, can be extended for more general action spaces which has a hierarchical structure. We present an instance of the hybrid actor-critic architecture based on proximal policy optimization (PPO), which we refer to as hybrid proximal policy optimization (H-PPO). Our experiments test H-PPO on a collection of tasks with parameterized action space, where H-PPO demonstrates superior performance over previous methods of parameterized action reinforcement learning.

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AAAI Conference 2019 Conference Paper

Large-Scale Interactive Recommendation with Tree-Structured Policy Gradient

  • Haokun Chen
  • Xinyi Dai
  • Han Cai
  • Weinan Zhang
  • Xuejian Wang
  • Ruiming Tang
  • Yuzhou Zhang
  • Yong Yu

Reinforcement learning (RL) has recently been introduced to interactive recommender systems (IRS) because of its nature of learning from dynamic interactions and planning for longrun performance. As IRS is always with thousands of items to recommend (i. e. , thousands of actions), most existing RLbased methods, however, fail to handle such a large discrete action space problem and thus become inefficient. The existing work that tries to deal with the large discrete action space problem by utilizing the deep deterministic policy gradient framework suffers from the inconsistency between the continuous action representation (the output of the actor network) and the real discrete action. To avoid such inconsistency and achieve high efficiency and recommendation effectiveness, in this paper, we propose a Tree-structured Policy Gradient Recommendation (TPGR) framework, where a balanced hierarchical clustering tree is built over the items and picking an item is formulated as seeking a path from the root to a certain leaf of the tree. Extensive experiments on carefully-designed environments based on two real-world datasets demonstrate that our model provides superior recommendation performance and significant efficiency improvement over state-of-the-art methods.

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AAAI Conference 2018 Conference Paper

Efficient Architecture Search by Network Transformation

  • Han Cai
  • Tianyao Chen
  • Weinan Zhang
  • Yong Yu
  • Jun Wang

Techniques for automatically designing deep neural network architectures such as reinforcement learning based approaches have recently shown promising results. However, their success is based on vast computational resources (e. g. hundreds of GPUs), making them difficult to be widely used. A noticeable limitation is that they still design and train each network from scratch during the exploration of the architecture space, which is highly inefficient. In this paper, we propose a new framework toward efficient architecture search by exploring the architecture space based on the current network and reusing its weights. We employ a reinforcement learning agent as the meta-controller, whose action is to grow the network depth or layer width with function-preserving transformations. As such, the previously validated networks can be reused for further exploration, thus saves a large amount of computational cost. We apply our method to explore the architecture space of the plain convolutional neural networks (no skip-connections, branching etc.) on image benchmark datasets (CIFAR-10, SVHN) with restricted computational resources (5 GPUs). Our method can design highly competitive networks that outperform existing networks using the same design scheme. On CIFAR-10, our model without skip-connections achieves 4. 23% test error rate, exceeding a vast majority of modern architectures and approaching DenseNet. Furthermore, by applying our method to explore the DenseNet architecture space, we are able to achieve more accurate networks with fewer parameters.

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IJCAI Conference 2018 Conference Paper

Learning to Design Games: Strategic Environments in Reinforcement Learning

  • Haifeng Zhang
  • Jun Wang
  • Zhiming Zhou
  • Weinan Zhang
  • Yin Wen
  • Yong Yu
  • Wenxin Li

In typical reinforcement learning (RL), the environment is assumed given and the goal of the learning is to identify an optimal policy for the agent taking actions through its interactions with the environment. In this paper, we extend this setting by considering the environment is not given, but controllable and learnable through its interaction with the agent at the same time. This extension is motivated by environment design scenarios in the real-world, including game design, shopping space design and traffic signal design. Theoretically, we find a dual Markov decision process (MDP) w. r. t. the environment to that w. r. t. the agent, and derive a policy gradient solution to optimizing the parametrized environment. Furthermore, discontinuous environments are addressed by a proposed general generative framework. Our experiments on a Maze game design task show the effectiveness of the proposed algorithms in generating diverse and challenging Mazes against various agent settings.

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AAAI Conference 2018 Conference Paper

Long Text Generation via Adversarial Training with Leaked Information

  • Jiaxian Guo
  • Sidi Lu
  • Han Cai
  • Weinan Zhang
  • Yong Yu
  • Jun Wang

Automatically generating coherent and semantically meaningful text has many applications in machine translation, dialogue systems, image captioning, etc. Recently, by combining with policy gradient, Generative Adversarial Nets (GAN) that use a discriminative model to guide the training of the generative model as a reinforcement learning policy has shown promising results in text generation. However, the scalar guiding signal is only available after the entire text has been generated and lacks intermediate information about text structure during the generative process. As such, it limits its success when the length of the generated text samples is long (more than 20 words). In this paper, we propose a new framework, called LeakGAN, to address the problem for long text generation. We allow the discriminative net to leak its own high-level extracted features to the generative net to further help the guidance. The generator incorporates such informative signals into all generation steps through an additional MANAGER module, which takes the extracted features of current generated words and outputs a latent vector to guide the WORKER module for next-word generation. Our extensive experiments on synthetic data and various realworld tasks with Turing test demonstrate that LeakGAN is highly effective in long text generation and also improves the performance in short text generation scenarios. More importantly, without any supervision, LeakGAN would be able to implicitly learn sentence structures only through the interaction between MANAGER and WORKER.

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AAAI Conference 2018 System Paper

MAgent: A Many-Agent Reinforcement Learning Platform for Artificial Collective Intelligence

  • Lianmin Zheng
  • Jiacheng Yang
  • Han Cai
  • Ming Zhou
  • Weinan Zhang
  • Jun Wang
  • Yong Yu

We introduce MAgent, a platform to support research and development of many-agent reinforcement learning. Unlike previous research platforms on single or multi-agent reinforcement learning, MAgent focuses on supporting the tasks and the applications that require hundreds to millions of agents. Within the interactions among a population of agents, it enables not only the study of learning algorithms for agents’ optimal polices, but more importantly, the observation and understanding of individual agent’s behaviors and social phenomena emerging from the AI society, including communication languages, leaderships, altruism. MAgent is highly scalable and can host up to one million agents on a single GPU server. MAgent also provides flexible configurations for AI researchers to design their customized environments and agents. In this demo, we present three environments designed on MAgent and show emerged collective intelligence by learning from scratch.

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AAAI Conference 2018 Conference Paper

Neural Link Prediction over Aligned Networks

  • Xuezhi Cao
  • Haokun Chen
  • Xuejian Wang
  • Weinan Zhang
  • Yong Yu

Link prediction is a fundamental problem with a wide range of applications in various domains, which predicts the links that are not yet observed or the links that may appear in the future. Most existing works in this field only focus on modeling a single network, while real-world networks are actually aligned with each other. Network alignments contain valuable additional information for understanding the networks, and provide a new direction for addressing data insufficiency and alleviating cold start problem. However, there are rare works leveraging network alignments for better link prediction. Besides, neural network is widely employed in various domains while its capability of capturing high-level patterns and correlations for link prediction problem has not been adequately researched yet. Hence, in this paper we target at link prediction over aligned networks using neural networks. The major challenge is the heterogeneousness of the considered networks, as the networks may have different characteristics, link purposes, etc. To overcome this, we propose a novel multi-neural-network framework MNN, where we have one individual neural network for each heterogeneous target or feature while the vertex representations are shared. We further discuss training methods for the multi-neural-network framework. Extensive experiments demonstrate that MNN outperforms the state-of-the-art methods and achieves 3% to 5% relative improvement of AUC score across different settings, particularly over 8% for cold start scenarios.

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AAAI Conference 2018 Conference Paper

Wasserstein Distance Guided Representation Learning for Domain Adaptation

  • Jian Shen
  • Yanru Qu
  • Weinan Zhang
  • Yong Yu

Domain adaptation aims at generalizing a high-performance learner on a target domain via utilizing the knowledge distilled from a source domain which has a different but related data distribution. One solution to domain adaptation is to learn domain invariant feature representations while the learned representations should also be discriminative in prediction. To learn such representations, domain adaptation frameworks usually include a domain invariant representation learning approach to measure and reduce the domain discrepancy, as well as a discriminator for classification. Inspired by Wasserstein GAN, in this paper we propose a novel approach to learn domain invariant feature representations, namely Wasserstein Distance Guided Representation Learning (WD- GRL). WDGRL utilizes a neural network, denoted by the domain critic, to estimate empirical Wasserstein distance between the source and target samples and optimizes the feature extractor network to minimize the estimated Wasserstein distance in an adversarial manner. The theoretical advantages of Wasserstein distance for domain adaptation lie in its gradient property and promising generalization bound. Empirical studies on common sentiment and image classification adaptation datasets demonstrate that our proposed WDGRL outperforms the state-of-the-art domain invariant representation learning approaches.

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IJCAI Conference 2017 Conference Paper

Aggregating Crowd Wisdoms with Label-aware Autoencoders

  • Li'ang Yin
  • Jianhua Han
  • Weinan Zhang
  • Yong Yu

Aggregating crowd wisdoms takes multiple labels from various sources and infers true labels for objects. Recent research work makes progress by learning source credibility from data and roughly form three kinds of modeling frameworks: weighted majority voting, trust propagation, and generative models. In this paper, we propose a novel framework named Label-Aware Autoencoders (LAA) to aggregate crowd wisdoms. LAA integrates a classifier and a reconstructor into a unified model to infer labels in an unsupervised manner. Analogizing classical autoencoders, we can regard the classifier as an encoder, the reconstructor as a decoder, and inferred labels as latent features. To the best of our knowledge, it is the first trial to combine label aggregation with autoencoders. We adopt networks to implement the classifier and the reconstructor which have the potential to automatically learn underlying patterns of source credibility. To further improve inference accuracy, we introduce object ambiguity and latent aspects into LAA. Experiments on three real-world datasets show that proposed models achieve impressive inference accuracy improvement over state-of-the-art models.

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AAAI Conference 2017 Conference Paper

SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient

  • Lantao Yu
  • Weinan Zhang
  • Jun Wang
  • Yong Yu

As a new way of training generative models, Generative Adversarial Net (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued data. However, it has limitations when the goal is for generating sequences of discrete tokens. A major reason lies in that the discrete outputs from the generative model make it difficult to pass the gradient update from the discriminative model to the generative model. Also, the discriminative model can only assess a complete sequence, while for a partially generated sequence, it is non-trivial to balance its current score and the future one once the entire sequence has been generated. In this paper, we propose a sequence generation framework, called SeqGAN, to solve the problems. Modeling the data generator as a stochastic policy in reinforcement learning (RL), SeqGAN bypasses the generator differentiation problem by directly performing gradient policy update. The RL reward signal comes from the GAN discriminator judged on a complete sequence, and is passed back to the intermediate state-action steps using Monte Carlo search. Extensive experiments on synthetic data and real-world tasks demonstrate significant improvements over strong baselines.

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AAAI Conference 2014 Conference Paper

Learning Word Representation Considering Proximity and Ambiguity

  • Lin Qiu
  • Yong Cao
  • Zaiqing Nie
  • Yong Yu
  • Yong Rui

Distributed representations of words (aka word embedding) have proven helpful in solving natural language processing (NLP) tasks. Training distributed representations of words with neural networks has lately been a major focus of researchers in the field. Recent work on word embedding, the Continuous Bag-of-Words (CBOW) model and the Continuous Skip-gram (Skip-gram) model, have produced particularly impressive results, significantly speeding up the training process to enable word representation learning from largescale data. However, both CBOW and Skip-gram do not pay enough attention to word proximity in terms of model or word ambiguity in terms of linguistics. In this paper, we propose Proximity-Ambiguity Sensitive (PAS) models (i. e. PAS CBOW and PAS Skip-gram) to produce high quality distributed representations of words considering both word proximity and ambiguity. From the model perspective, we introduce proximity weights as parameters to be learned in PAS CBOW and used in PAS Skip-gram. By better modeling word proximity, we reveal the strength of pooling-structured neural networks in word representation learning. The proximitysensitive pooling layer can also be applied to other neural network applications that employ pooling layers. From the linguistics perspective, we train multiple representation vectors per word. Each representation vector corresponds to a particular group of POS tags of the word. By using PAS models, we achieved a 16. 9% increase in accuracy over state-of-theart models.

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TIST Journal 2012 Journal Article

EachWiki

  • Haofen Wang
  • Linyun Fu
  • Wei Jin
  • Yong Yu

Wikipedia, one of the best-known wikis and the world’s largest free online encyclopedia, has embraced the power of collaborative editing to harness collective intelligence. However, using such a wiki to create high-quality articles is not as easy as people imagine, given for instance the difficulty of reusing knowledge already available in Wikipedia. As a result, the heavy burden of upbuilding and maintaining the ever-growing online encyclopedia still rests on a small group of people. In this article, we aim at facilitating wiki authoring by providing annotation recommendations, thus lightening the burden of both contributors and administrators. We leverage the collective wisdom of the users by exploiting Semantic Web technologies with Wikipedia data and adopt a unified algorithm to support link, category, and semantic relation recommendation. A prototype system named EachWiki is proposed and evaluated. The experimental results show that it has achieved considerable improvements in terms of effectiveness, efficiency and usability. The proposed approach can also be applied to other wiki-based collaborative editing systems.

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AAAI Conference 2012 Conference Paper

Large Scale Temporal RDFS Reasoning Using MapReduce

  • Chang Liu
  • Guilin Qi
  • Yong Yu

In this work, we build a large scale reasoning engine under temporal RDFS semantics using MapReduce. We identify the major challenges of applying MapReduce framework to reason over temporal information, and present our solutions to tackle them.

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AAAI Conference 2012 Conference Paper

Sembler: Ensembling Crowd Sequential Labeling for Improved Quality

  • Xian Wu
  • Wei Fan
  • Yong Yu

Many natural language processing tasks, such as named entity recognition (NER), part of speech (POS) tagging, word segmentation, and etc. , can be formulated as sequential data labeling problems. Building a sound labeler requires very large number of correctly labeled training examples, which may not always be possible. On the other hand, crowdsourcing provides an inexpensive yet efficient alternative to collect manual sequential labeling from non-experts. However the quality of crowd labeling cannot be guaranteed, and three kinds of errors are typical: (1) incorrect annotations due to lack of expertise (e. g. , labeling gene names from plain text requires corresponding domain knowledge); (2) ignored or omitted annotations due to carelessness or low confidence; (3) noisy annotations due to cheating or vandalism. To correct these mistakes, we present Sembler, a statistical model for ensembling crowd sequential labelings. Sembler considers three types of statistical information: (1) the majority agreement that proves the correctness of an annotation; (2) correct annotation that improves the credibility of the corresponding annotator; (3) correct annotation that enhances the correctness of other annotations which share similar linguistic or contextual features. We evaluate the proposed model on a real Twitter and a synthetical biological data set, and find that Sembler is particularly accurate when more than half of annotators make mistakes.

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JMLR Journal 2012 Journal Article

SVDFeature: A Toolkit for Feature-based Collaborative Filtering

  • Tianqi Chen
  • Weinan Zhang
  • Qiuxia Lu
  • Kailong Chen
  • Zhao Zheng
  • Yong Yu

In this paper we introduce SVDFeature, a machine learning toolkit for feature-based collaborative filtering. SVDFeature is designed to efficiently solve the feature-based matrix factorization. The feature-based setting allows us to build factorization models incorporating side information such as temporal dynamics, neighborhood relationship, and hierarchical information. The toolkit is capable of both rate prediction and collaborative ranking, and is carefully designed for efficient training on large-scale data set. Using this toolkit, we built solutions to win KDD Cup for two consecutive years. [abs] [ pdf ][ bib ] [ code ] &copy JMLR 2012. ( edit, beta )

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AAAI Conference 2011 Conference Paper

Analyzing and Predicting Not-Answered Questions in Community-based Question Answering Services

  • Lichun Yang
  • Shenghua Bao
  • Qingliang Lin
  • Xian Wu
  • Dingyi Han
  • Zhong Su
  • Yong Yu

This paper focuses on analyzing and predicting not-answered questions in Community based Question Answering (CQA) services, such as Yahoo! Answers. In CQA, users express their information needs by submitting questions and await answers from other users. One of the key problems of this pattern is that sometimes no one helps to give answers. In this paper, we analyze the not-answered questions and give a first try of predicting whether questions will receive answers. More specifically, we first analyze the questions of Yahoo! Answers based on the features selected from different perspectives. Then, we formalize the prediction problem as supervised learning task and leverage the proposed features to make predictions. Extensive experiments are made on 76, 251 questions collected from Yahoo! Answers.

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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

Leveraging Unlabeled Data to Scale Blocking for Record Linkage

  • Yunbo Cao
  • Zhiyuan Chen
  • Jiamin Zhu
  • Pei Yue
  • Chin-Yew Lin
  • Yong Yu

Record linkage is the process of matching records between two (or multiple) data sets that represent the same real-world entity. An exhaustive record linkage process involves computing the similarities between all pairs of records, which can be very expensive for large data sets. Blocking techniques alleviate this problem by dividing the records into blocks and only comparing records within the same block. To be adaptive from domain to domain, one category of blocking technique formalizes 'construction of blocking scheme' as a machine learning problem. In the process of learning the best blocking scheme, previous learning-based techniques utilize only a set of labeled data. However, since the set of labeled data is usually not large enough to well characterize the unseen (unlabeled) data, the resultant blocking scheme may poorly perform on the unseen data by generating too many candidate matches. To address that, in this paper, we propose to utilize unlabeled data (in addition to labeled data) for learning blocking schemes. Our experimental results show that using unlabeled data in learning can remarkably reduce the number of candidate matches while keeping the same level of coverage for true matches.

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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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KER Journal 2003 Journal Article

OntoVote: a scalable distributed vote-collecting mechanism for ontology drift on a P2P platform

  • YANFENG GE
  • Yong Yu
  • Xing Zhu
  • Shen Huang
  • Min Xu

Ontologies provide potential support for knowledge and content management on a P2P platform. Although we can design ontologies beforehand for an application, it is argued that in P2P environments static or predefined ontologies cannot satisfy the ever-changing requirements of all users. So we propose every user should make proposals for what kind of ontology is the most apt to his need. Collecting all these proposals (or votes) helps the drift of ontologies. This paper introduces OntoVote, a scalable distributed vote-collecting mechanism based on application-level broadcast trees, and describes how OntoVote can be applied to ontology drift on a P2P platform by discussing several problems involved in the voting process.

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v2026.09.13