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Qin Zhang

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27 papers

2 author rows

EAAI Journal 2026 Journal Article

Deep multimodal fusion of spectral and visual data for laser welding defect classification

Qin Zhang
Zhongyou Zhao
Zhenmin Wang
Zixuan Wan
Hui-ping Wang
Guangze Li

Laser welding defect detection requires accurate interpretation of heterogeneous signals, in which weld images and spectral time-series data provide complementary information. However, effectively integrating these two types of data remains challenging. In this study, we construct a multimodal dataset for automotive battery busbar welding and propose a fusion framework based on cross-attention. Weld seams are first segmented using a convolutional network to suppress background interference, and informative spectral channels are selected through correlation analysis. Visual and spectral features are then jointly modeled by means of an inverted spectral embedding module and a vision-to-spectrum cross-attention mechanism, enabling fine-grained multimodal interaction. The proposed artificial intelligence method achieves an overall accuracy rate of 99. 2%, which further improves to 100. 0% with an increased spectral embedding dimension, outperforming all single-modality and baseline fusion approaches. Extensive ablation studies validate the benefits of segmentation, channel selection, and embedding design. Moreover, experiments on publicly available industrial defect datasets confirm the generalizability and robustness of our approach across diverse industrial defect inspection scenarios. To the best of our knowledge, this is the first work to apply cross-attention for fusing image and spectral data in laser welding, offering a novel and practical solution for multimodal industrial inspection.

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AAAI Conference 2026 Conference Paper

LR-AdaInSeg:Adaptive Instance Segmentation of Incomplete 3D Scenes Driven by Low-Rank Networks

Qin Zhang
Kun Zhou
Xulun Ye

3D full-scene segmentation technology has demonstrated great potential driven by large models, but it often faces challenges of incomplete scenes and identification of invisible classes in practical applications. To address this, we propose the LR-AdaInSeg method, which significantly enhances the model’s generalization ability in incomplete scenes through two key innovations: First, we design a Bayesian Low-Rank Module, which effectively solves the problem of feature space redundancy through dynamic optimization of the network structure, improving adaptability to incomplete scenes. Second, we combine graph contrastive clustering with the Low-Rank module, leveraging its robust feature representation capability to achieve accurate differentiation of invisible classes. In terms of implementation, we build a multi-scale feature extraction framework based on the 3D U-Net and utilize the 3D prompt points and their 2D masks as supervisory signals to achieve effective fusion of geometric and semantic information. Experiments show that our method achieves advanced performance on multiple benchmarks such as ScanNet, particularly excelling in handling incomplete scenes and invisible class objects.

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JBHI Journal 2026 Journal Article

Vision Sensing-Driven Intelligent Ocular Disease Detection Using Conformer-Based Dual Fusion

Zhiwei Guo
Qin Zhang
Peng Xu
Yu Shen
Chinmay Chakraborty
Osama Alfarraj
Keping Yu

The deep vision sensing has been a practical tool in early disease detection, and this work aims at an important branch of ocular disease recognition. Although a number of researchers had paid attention to it during past years, fine-grained ocular feature extraction always remains a challenge. To handle with this issue, this work benefits from comprehensive ability of the convolution-Transformer structure (Conformer), and proposes vision sensing-driven intelligent ocular disease detection using conformer-based dual fusion. On the one hand, the proposal combines technical advantages of convolution and visual Transformer to more accurately fuse local subtle features and global representation information in images. On the other hand, the proposal significantly improves accuracy and robustness of the model by optimizing depth and width. Simulation experiments on real-world ocular disease image datasets show that the proposed model exhibits higher performance in ocular disease detection compared to other methods. Numerical results show that it improves the detection accuracy by 1% to 3. 7% compared to several mainstream baseline methods. This research result not only promotes the development of ocular disease detection, but also provides more reliable technical support for accurate diagnosis of ophthalmic diseases.

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ICML Conference 2025 Conference Paper

EPIC: Efficient Position-Independent Caching for Serving Large Language Models

Junhao Hu
Wenrui Huang
Weidong Wang
Haoyi Wang
Tiancheng Hu
Qin Zhang
Hao Feng
Xusheng Chen

Large Language Models (LLMs) show great capabilities in a wide range of applications, but serving them efficiently becomes increasingly challenging as requests (prompts) become more complex. Context caching improves serving performance by reusing Key-Value (KV) vectors, the intermediate representations of tokens that are repeated across requests. However, existing context caching requires exact prefix matches across requests, limiting reuse cases in settings such as few-shot learning and retrieval-augmented generation, where immutable content (e. g. , documents) remains unchanged across requests but is preceded by varying prefixes. Position-Independent Caching (PIC) addresses this issue by enabling modular reuse of the KV vectors regardless of prefixes. We formalize PIC and advance prior work by introducing EPIC, a serving system incorporating our new LegoLink algorithm, which mitigates the inappropriate “attention sink” effect at every document beginning, to maintain accuracy with minimal computation. Experiments show that EPIC achieves up to 8$\times$ improvements in Time-To-First-Token (TTFT) and 7$\times$ throughput gains over existing systems, with negligible or no accuracy loss.

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AAAI Conference 2025 Conference Paper

TGLsta: Low-resource Textual Graph Learning with Semantic and Topological Awareness via LLMs

Qin Zhang
Xiaowei Li
Ziqi Liu
Xiaochen Fan
Xiaojun Chen
Shirui Pan

Textual Graphs (TGs) present a graph-based representation of textual data and find wide applications in real-world scenarios, such as citation networks, knowledge graphs, and social networks. While the traditional "pre-train, fine-tune" framework effectively addresses tasks requiring abundant labeled data, it falls short in scenarios with limited resource or zero-shot learning capabilities, particularly in low-resource textual graph node classification. Additionally, prevalent approaches that convert text nodes into shallow or manually engineered features fail to capture the rich semantic nuances within the text. The conventional methods often neglect the fusion of semantic and topological information, resulting in suboptimal model learning. To overcome these challenges, we proposed a novel method of low-resource textual graph node classification based on large language models, i.e., Textual graph learning with semantic and topological awareness (TGLsta), which comprehensively explores the semantic information, near neighborhood information, and the topology information in textual graphs, where these components are the most important information source contained in textual graphs. Graph prompt tuning for both zero- and few-shot textual graph node classification is further introduced.

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IS Journal 2025 Journal Article

The Rise of Small Language Models

Qin Zhang
Ziqi Liu
Shirui Pan

Large language models (LLMs), such as GPT and LLAMA, exhibit exceptional comprehension and reasoning capabilities across a wide range of tasks, which are a result of the extensive corpora and the enormous number of parameters in a model. However, their size can pose significant challenges for deployment, particularly on resource-constrained devices. For issues that degrade the user experience, such as efficiency, latency, safety, and privacy, small language models (SLMs) offer a solution. This article begins by outlining the key principles behind SLMs and the reasons for their importance in the field. Subsequently, we discuss the methods used to develop SLMs and explore the collaboration between SLMs and LLMs. By exploring the pathways for harnessing the unique capabilities of SLMs and optimizing their integration with LLMs, it contributes to the ongoing discussion on their application and collaboration in natural language processing and offers insights for advancement and innovation in the field.

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AAAI Conference 2025 Conference Paper

Uncertainty-Aware Contrastive Learning with Hard Negative Sampling for Code Search Tasks

Han Liu
Jiaqing Zhan
Qin Zhang

Code search is a highly required technique for software development. In recent years, the rapid development of transformer-based language models has made it increasingly more popular to adapt a pre-trained language model to a code search task, where contrastive learning is typically adopted to semantically align user queries and codes in an embedding space. Considering that the same semantic meaning can be presented using diverse language styles in user queries and codes, the representation of queries and codes in an embedding space may thus be non-deterministic. To address the above-specified point, this paper proposes an uncertainty-aware contrastive learning approach for code search. Specifically, for both queries and codes, we design an uncertainty learning strategy to produce diverse embeddings by learning to transform the original inputs into Gaussian distributions and then taking a reparameterization trick. We also design a hard negative sampling strategy to construct query-code pairs for improving the effectiveness of uncertainty-aware contrastive learning. The experimental results indicate that our approach outperforms 10 baseline methods on a large code search dataset with six programming languages. The results also show that our strategies of uncertainty learning and hard negative sampling can really help enhance the representation of queries and codes leading to an improvement of the code search performance.

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TMLR Journal 2024 Journal Article

Affordable Generative Agents

Yangbin Yu
Qin Zhang
Junyou Li
Qiang Fu
Deheng Ye

The emergence of large language models (LLMs) has significantly advanced the simulation of believable interactive agents. However, the substantial cost on maintaining the prolonged agent interactions poses challenge over the deployment of believable LLM-based agents. Therefore, in this paper, we develop Affordable Generative Agents (AGA), a framework for enabling the generation of believable and low-cost interactions on both agent-environment and inter-agents. Specifically, for agent-environment interactions, we substitute repetitive LLM inferences with learned policies; while for inter-agent interactions, we model the social relationships between agents and compress auxiliary dialogue information. Extensive experiments on multiple environments show the effectiveness and efficiency of our proposed framework. Also, we delve into the mechanisms of emergent believable behaviors lying in LLM agents, demonstrating that agents can only generate finite behaviors in fixed environments, based upon which, we understand ways to facilitate emergent interaction behaviors. Our code is publicly available at: https://github.com/AffordableGenerativeAgents/Affordable-Generative-Agents.

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AAAI Conference 2024 Conference Paper

Communication-Efficient Collaborative Regret Minimization in Multi-Armed Bandits

Nikolai Karpov
Qin Zhang

In this paper, we study the collaborative learning model, which concerns the tradeoff between parallelism and communication overhead in multi-agent multi-armed bandits. For regret minimization in multi-armed bandits, we present the first set of tradeoffs between the number of rounds of communication between the agents and the regret of the collaborative learning process.

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IJCAI Conference 2024 Conference Paper

CONC: Complex-noise-resistant Open-set Node Classification with Adaptive Noise Detection

Qin Zhang
Jiexin Lu
Xiaowei Li
Huisi Wu
Shirui Pan
Junyang Chen

As a popular task in graph learning, node classification seeks to assign labels to nodes, taking into account both their features and connections. However, an important challenge for its application in real-world scenarios is the presence of newly-emerged out-of-distribution samples and noisy samples, which affect the quality and robustness of learned classifiers. Out-of-distribution (OOD) samples are often found in both the training and testing phases. Such samples don’t belong to any known categories. These OOD samples are considered as outliers (OOD noise) when they appear during training, and are recognized as open-set samples during the testing. Meanwhile, in-distribution (IND) noisy data, i. e. , known class samples with wrong labels, are also prevalent and inevitably degrade a model’s performance. The challenge of open-set learning with complex IND and OOD noise remains largely unexplored, particularly when dealing with non-IID graph data. To address these challenges, this paper introduces a novel complex-noise-resistant open-set node classification approach, designed for open-set graph data containing both IND and OOD noisy nodes. Specifically, a trustworthiness learner is adopted to learn the trustworthiness rates of the feature and label for each node while a decoder and an open-set classifier are trained to reconstruct the structure of a node and to predict its category simultaneously with the guidance of node trustworthiness. The experimental results demonstrate the superiority of our method.

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NeurIPS Conference 2024 Conference Paper

EGonc : Energy-based Open-Set Node Classification with substitute Unknowns

Qin Zhang
Zelin Shi
Shirui Pan
Junyang Chen
Huisi Wu
Xiaojun Chen

Open-set Classification (OSC) is a critical requirement for safely deploying machine learning models in the open world, which aims to classify samples from known classes and reject samples from out-of-distribution (OOD). Existing methods exploit the feature space of trained network and attempt at estimating the uncertainty in the predictions. However, softmax-based neural networks are found to be overly confident in their predictions even on data they have never seen before andthe immense diversity of the OOD examples also makes such methods fragile. To this end, we follow the idea of estimating the underlying density of the training data to decide whether a given input is close to the in-distribution (IND) data and adopt Energy-based models (EBMs) as density estimators. A novel energy-based generative open-set node classification method, \textit{EGonc}, is proposed to achieve open-set graph learning. Specifically, we generate substitute unknowns to mimic the distribution of real open-set samples firstly, based on the information of graph structures. Then, an additional energy logit representing the virtual OOD class is learned from the residual of the feature against the principal space, and matched with the original logits by a constant scaling. This virtual logit serves as the indicator of OOD-ness. EGonc has nice theoretical properties that guarantee an overall distinguishable margin between the detection scores for IND and OOD samples. Comprehensive experimental evaluations of EGonc also demonstrate its superiority.

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TMLR Journal 2024 Journal Article

More Agents Is All You Need

Junyou Li
Qin Zhang
Yangbin Yu
Qiang Fu
Deheng Ye

We find that, simply via a sampling-and-voting method, the performance of large language models (LLMs) scales with the number of agents instantiated. Also, this method, termed as Agent Forest, is orthogonal to existing complicated methods to further enhance LLMs, while the degree of enhancement is correlated to the task difficulty. We conduct comprehensive experiments on a wide range of LLM benchmarks to verify the presence of our finding, and to study the properties that can facilitate its occurrence. Our code is publicly available at: https://github.com/MoreAgentsIsAllYouNeed/AgentForest

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AAAI Conference 2024 Conference Paper

Multi-Level Cross-Modal Alignment for Image Clustering

Liping Qiu
Qin Zhang
Xiaojun Chen
Shaotian Cai

Recently, the cross-modal pretraining model has been employed to produce meaningful pseudo-labels to supervise the training of an image clustering model. However, numerous erroneous alignments in a cross-modal pretraining model could produce poor-quality pseudo labels and degrade clustering performance. To solve the aforementioned issue, we propose a novel Multi-level Cross-modal Alignment method to improve the alignments in a cross-modal pretraining model for downstream tasks, by building a smaller but better semantic space and aligning the images and texts in three levels, i.e., instance-level, prototype-level, and semantic-level. Theoretical results show that our proposed method converges, and suggests effective means to reduce the expected clustering risk of our method. Experimental results on five benchmark datasets clearly show the superiority of our new method.

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AAAI Conference 2024 Conference Paper

ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning

Qin Zhang
Xiaowei Li
Jiexin Lu
Liping Qiu
Shirui Pan
Xiaojun Chen
Junyang Chen

Open-set graph learning is a practical task that aims to classify the known class nodes and to identify unknown class samples as unknowns. Conventional node classification methods usually perform unsatisfactorily in open-set scenarios due to the complex data they encounter, such as out-of-distribution (OOD) data and in-distribution (IND) noise. OOD data are samples that do not belong to any known classes. They are outliers if they occur in training (OOD noise), and open-set samples if they occur in testing. IND noise are training samples which are assigned incorrect labels. The existence of IND noise and OOD noise is prevalent, which usually cause the ambiguity problem, including the intra-class variety problem and the inter-class confusion problem. Thus, to explore robust open-set learning methods is necessary and difficult, and it becomes even more difficult for non-IID graph data. To this end, we propose a unified framework named ROG_PL to achieve robust open-set learning on complex noisy graph data, by introducing prototype learning. In specific, ROG_PL consists of two modules, i.e., denoising via label propagation and open-set prototype learning via regions. The first module corrects noisy labels through similarity-based label propagation and removes low-confidence samples, to solve the intra-class variety problem caused by noise. The second module learns open-set prototypes for each known class via non-overlapped regions and remains both interior and border prototypes to remedy the inter-class confusion problem. The two modules are iteratively updated under the constraints of classification loss and prototype diversity loss. To the best of our knowledge, the proposed ROG_PL is the first robust open-set node classification method for graph data with complex noise. Experimental evaluations of ROG_PL on several benchmark graph datasets demonstrate that it has good performance.

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ICLR Conference 2024 Conference Paper

Threshold-Consistent Margin Loss for Open-World Deep Metric Learning

Qin Zhang
Linghan Xu
Jun Fang
Qingming Tang
Ying Nian Wu
Joseph Tighe
Yifan Xing

Existing losses used in deep metric learning (DML) for image retrieval often lead to highly non-uniform intra-class and inter-class representation structures across test classes and data distributions. When combined with the common practice of using a fixed threshold to declare a match, this gives rise to significant performance variations in terms of false accept rate (FAR) and false reject rate (FRR) across test classes and data distributions. We define this issue in DML as threshold inconsistency. In real-world applications, such inconsistency often complicates the threshold selection process when deploying large-scale image retrieval systems. To measure this inconsistency, we propose a novel variance-based metric called Operating-Point-Inconsistency-Score (OPIS) that quantifies the variance in the operating characteristics across classes. Using the OPIS metric, we find that achieving high accuracy levels in a DML model does not automatically guarantee threshold consistency. In fact, our investigation reveals a Pareto frontier in the high-accuracy regime, where existing methods to improve accuracy often lead to degradation in threshold consistency. To address this trade-off, we introduce the Threshold-Consistent Margin (TCM) loss, a simple yet effective regularization technique that promotes uniformity in representation structures across classes by selectively penalizing hard sample pairs. Large-scale experiments demonstrate TCM's effectiveness in enhancing threshold consistency while preserving accuracy, simplifying the threshold selection process in practical DML settings.

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AAAI Conference 2023 Conference Paper

Communication-Efficient Collaborative Best Arm Identification

Nikolai Karpov
Qin Zhang

We investigate top-m arm identification, a basic problem in bandit theory, in a multi-agent learning model in which agents collaborate to learn an objective function. We are interested in designing collaborative learning algorithms that achieve maximum speedup (compared to single-agent learning algorithms) using minimum communication cost, as communication is frequently the bottleneck in multi-agent learning. We give both algorithmic and impossibility results, and conduct a set of experiments to demonstrate the effectiveness of our algorithms.

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AAAI Conference 2023 Conference Paper

Evaluating Model-Free Reinforcement Learning toward Safety-Critical Tasks

Linrui Zhang
Qin Zhang
Li Shen
Bo Yuan
Xueqian Wang
Dacheng Tao

Safety comes first in many real-world applications involving autonomous agents. Despite a large number of reinforcement learning (RL) methods focusing on safety-critical tasks, there is still a lack of high-quality evaluation of those algorithms that adheres to safety constraints at each decision step under complex and unknown dynamics. In this paper, we revisit prior work in this scope from the perspective of state-wise safe RL and categorize them as projection-based, recovery-based, and optimization-based approaches, respectively. Furthermore, we propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection. This novel technique explicitly enforces hard constraints via the deep unrolling architecture and enjoys structural advantages in navigating the trade-off between reward improvement and constraint satisfaction. To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit, a toolkit that provides off-the-shelf interfaces and evaluation utilities for safety-critical tasks. We then perform a comparative study of the involved algorithms on six benchmarks ranging from robotic control to autonomous driving. The empirical results provide an insight into their applicability and robustness in learning zero-cost-return policies without task-dependent handcrafting. The project page is available at https://sites.google.com/view/saferlkit.

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IJCAI Conference 2023 Conference Paper

G2Pxy: Generative Open-Set Node Classification on Graphs with Proxy Unknowns

Qin Zhang
Zelin Shi
Xiaolin Zhang
Xiaojun Chen
Philippe Fournier-Viger
Shirui Pan

Node classification is the task of predicting the labels of unlabeled nodes in a graph. State-of-the-art methods based on graph neural networks achieve excellent performance when all labels are available during training. But in real-life, models are of ten applied on data with new classes, which can lead to massive misclassification and thus significantly degrade performance. Hence, developing open-set classification methods is crucial to determine if a given sample belongs to a known class. Existing methods for open-set node classification generally use transductive learning with part or all of the features of real unseen class nodes to help with open-set classification. In this paper, we propose a novel generative open-set node classification method, i. e. , G2Pxy, which follows a stricter inductive learning setting where no information about unknown classes is available during training and validation. Two kinds of proxy unknown nodes, inter-class unknown proxies and external unknown proxies are generated via mixup to efficiently anticipate the distribution of novel classes. Using the generated proxies, a closed-set classifier can be transformed into an open-set one, by augmenting it with an extra proxy classifier. Under the constraints of both cross entropy loss and complement entropy loss, G2Pxy achieves superior effectiveness for unknown class detection and known class classification, which is validated by experiments on bench mark graph datasets. Moreover, G2Pxy does not have specific requirement on the GNN architecture and shows good generalizations.

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AAAI Conference 2023 Conference Paper

Semantic-Enhanced Image Clustering

Shaotian Cai
Liping Qiu
Xiaojun Chen
Qin Zhang
Longteng Chen

Image clustering is an important and open challenging task in computer vision. Although many methods have been proposed to solve the image clustering task, they only explore images and uncover clusters according to the image features, thus being unable to distinguish visually similar but semantically different images. In this paper, we propose to investigate the task of image clustering with the help of visual-language pre-training model. Different from the zero-shot setting, in which the class names are known, we only know the number of clusters in this setting. Therefore, how to map images to a proper semantic space and how to cluster images from both image and semantic spaces are two key problems. To solve the above problems, we propose a novel image clustering method guided by the visual-language pre-training model CLIP, named Semantic-Enhanced Image Clustering (SIC). In this new method, we propose a method to map the given images to a proper semantic space first and efficient methods to generate pseudo-labels according to the relationships between images and semantics. Finally, we propose to perform clustering with consistency learning in both image space and semantic space, in a self-supervised learning fashion. The theoretical result of convergence analysis shows that our proposed method can converge at a sublinear speed. Theoretical analysis of expectation risk also shows that we can reduce the expectation risk by improving neighborhood consistency, increasing prediction confidence, or reducing neighborhood imbalance. Experimental results on five benchmark datasets clearly show the superiority of our new method.

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AAAI Conference 2022 Conference Paper

Deep Unsupervised Hashing with Latent Semantic Components

Qinghong Lin
Xiaojun Chen
Qin Zhang
Shaotian Cai
Wenzhe Zhao
Hongfa Wang

Deep unsupervised hashing has been appreciated in the regime of image retrieval. However, most prior arts failed to detect the semantic components and their relationships behind the images, which makes them lack discriminative power. To make up the defect, we propose a novel Deep Semantic Components Hashing (DSCH), which involves a common sense that an image normally contains a bunch of semantic components with homology and co-occurrence relationships. Based on this prior, DSCH regards the semantic components as latent variables under the Expectation- Maximization framework and designs a two-step iterative algorithm with the objective of maximum likelihood of training data. Firstly, DSCH constructs a semantic component structure by uncovering the fine-grained semantics components of images with a Gaussian Mixture Modal (GMM), where an image is represented as a mixture of multiple components, and the semantics co-occurrence are exploited. Besides, coarse-grained semantics components, are discovered by considering the homology relationships between finegrained components, and the hierarchy organization is then constructed. Secondly, DSCH makes the images close to their semantic component centers at both fine-grained and coarsegrained levels, and also makes the images share similar semantic components close to each other. Extensive experiments on three benchmark datasets demonstrate that the proposed hierarchical semantic components indeed facilitate the hashing model to achieve superior performance.

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AAAI Conference 2022 Conference Paper

Instance-Sensitive Algorithms for Pure Exploration in Multinomial Logit Bandit

Nikolai Karpov
Qin Zhang

Motivated by real-world applications such as fast fashion retailing and online advertising, the Multinomial Logit Bandit (MNL-bandit) is a popular model in online learning and operations research, and has attracted much attention in the past decade. In this paper, we give efficient algorithms for pure exploration in MNL-bandit. Our algorithms achieve instancesensitive pull complexities. We also complement the upper bounds by an almost matching lower bound.

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NeurIPS Conference 2020 Conference Paper

Batched Coarse Ranking in Multi-Armed Bandits

Nikolai Karpov
Qin Zhang

We study the problem of coarse ranking in the multi-armed bandits (MAB) setting, where we have a set of arms each of which is associated with an unknown distribution. The task is to partition the arms into clusters of predefined sizes, such that the mean of any arm in the $i$-th cluster is larger than that of any arm in the $j$-th cluster for any $j > i$. Coarse ranking generalizes a number of basic problems in MAB (e. g. , best arm identification) and has many real-world applications. We initiate the study of the problem in the batched model where we can only have a small number of policy changes. We study both the fixed budget and fixed confidence variants in MAB, and propose algorithms and prove impossibility results which together give almost tight tradeoffs between the total number of arms pulls and the number of policy changes. We have tested our algorithms in both real and synthetic data; our experimental results have demonstrated the efficiency of the proposed methods.

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NeurIPS Conference 2018 Conference Paper

A Practical Algorithm for Distributed Clustering and Outlier Detection

Jiecao Chen
Erfan Sadeqi Azer
Qin Zhang

We study the classic k-means/median clustering, which are fundamental problems in unsupervised learning, in the setting where data are partitioned across multiple sites, and where we are allowed to discard a small portion of the data by labeling them as outliers. We propose a simple approach based on constructing small summary for the original dataset. The proposed method is time and communication efficient, has good approximation guarantees, and can identify the global outliers effectively. To the best of our knowledge, this is the first practical algorithm with theoretical guarantees for distributed clustering with outliers. Our experiments on both real and synthetic data have demonstrated the clear superiority of our algorithm against all the baseline algorithms in almost all metrics.

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NeurIPS Conference 2018 Conference Paper

Tight Bounds for Collaborative PAC Learning via Multiplicative Weights

Jiecao Chen
Qin Zhang
Yuan Zhou

We study the collaborative PAC learning problem recently proposed in Blum et al. ~\cite{BHPQ17}, in which we have $k$ players and they want to learn a target function collaboratively, such that the learned function approximates the target function well on all players' distributions simultaneously. The quality of the collaborative learning algorithm is measured by the ratio between the sample complexity of the algorithm and that of the learning algorithm for a single distribution (called the overhead). We obtain a collaborative learning algorithm with overhead $O(\ln k)$, improving the one with overhead $O(\ln^2 k)$ in \cite{BHPQ17}. We also show that an $\Omega(\ln k)$ overhead is inevitable when $k$ is polynomial bounded by the VC dimension of the hypothesis class. Finally, our experimental study has demonstrated the superiority of our algorithm compared with the one in Blum et al. ~\cite{BHPQ17} on real-world datasets.

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NeurIPS Conference 2016 Conference Paper

Communication-Optimal Distributed Clustering

Jiecao Chen
He Sun
David Woodruff
Qin Zhang

Clustering large datasets is a fundamental problem with a number of applications in machine learning. Data is often collected on different sites and clustering needs to be performed in a distributed manner with low communication. We would like the quality of the clustering in the distributed setting to match that in the centralized setting for which all the data resides on a single site. In this work, we study both graph and geometric clustering problems in two distributed models: (1) a point-to-point model, and (2) a model with a broadcast channel. We give protocols in both models which we show are nearly optimal by proving almost matching communication lower bounds. Our work highlights the surprising power of a broadcast channel for clustering problems; roughly speaking, to cluster n points or n vertices in a graph distributed across s servers, for a worst-case partitioning the communication complexity in a point-to-point model is n*s, while in the broadcast model it is n + s. We implement our algorithms and demonstrate this phenomenon on real life datasets, showing that our algorithms are also very efficient in practice.

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IJCAI Conference 2016 Conference Paper

Unsupervised Feature Learning from Time Series

Qin Zhang
Jia Wu
Hong Yang
YingJie Tian
Chengqi Zhang

In this paper we study the problem of learning discriminative features (segments), often referred to as shapelets [Ye and Keogh, 2009] of time series, from unlabeled time series data. Discovering shapelets for time series classification has been widely studied, where many search-based algorithms are proposed to efficiently scan and select segments from a pool of candidates. However, such types of search-based algorithms may incur high time cost when the segment candidate pool is large. Alternatively, a recent work [Grabocka et al. , 2014] uses regression learning to directly learn, instead of searching for, shapelets from time series. Motivated by the above observations, we propose a new Unsupervised Shapelet Learning Model (USLM) to efficiently learn shapelets from unlabeled timeseries data. The corresponding learning function integrates the strengths of pseudo-class label, spectral analysis, shapelets regularization term and regularized least-squares to auto-learn shapelets, pseudo-class labels and classification boundaries simultaneously. A coordinate descent algorithm is used to iteratively solve the learning function. Experiments show that USLM outperforms search-based algorithms on real-world time series data.

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IROS Conference 2004 Conference Paper

Development of a transformational mobile robot to search victims under debris and rubbles

Katsuhiko Tabata
Akio Inaba
Qin Zhang
Hisanori Amano

Mobile robots are expected to support relief activities, after a great scale disaster. These robots need ability to move on/in rubble in order to gather information related to victims and conditions. However, there is no mobile robot which has enough movement ability. Mobile robots need big bodies in order to traverse on unstructured terrain composed of big rubbles, but they need small bodies in order to enter in narrow space. In this paper, we proposed novel concept of a transformational mobile robot in order to solve this inconsistency, and manufactured the proposed mechanism and evaluated its fundamental movement ability.

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