[Conference Paper][Journal Article]

Conference Paper

[AAAI] [AAMAS] [AISTATS] [CEC] [CIKM] [CVPR] [DSAA] [ECAI] [ICDM] [ICLR] [ICML] [IJCAI] [KDD] [MICCAI] [NeurIPS] [SDM] [UAI] [WSDM]

AAAI

• H. Sun, Z. Xie, X.-Y. Li, and M. Li. Cooperative and Adversarial Learning: Co-enhancing Discriminability and Transferability in Domain Adaptation. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), 2023.

• Y.-X. Huang, W.-Z. Dai, Y. Jiang, and Z.-H. Zhou. Enabling Knowledge Refinement upon New Concepts in Abductive Learning. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), 2023.

• F.-G. Han and Z.-Z. Zhang. Expert Data Augmentation in Imitation Learning (Student Abstract). In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), 2023.

• D.-X. Liu, X. Mu, and C. Qian. Human Assisted Learning by Evolutionary Multi-objective Optimization. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), 2023.

• Y.-C. Li, W.-J. Shen, B.-Y. Zhang, F. Mao, Z.-Z. Zhang, and Y. Yu. Learning Generalizable Batch Active Learning Strategies via Deep Q-Networks (Student Abstract). In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), 2023.

• R.-Z. Zhou, Z.-Z. Zhang, and Y. Yu. Model-based Offline Weighted Policy Optimization (Student Abstract). In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), 2023.

• W.-J. Liao, Z.-Z. Zhang, and Y. Yu. Policy-Independent Behavioral Metric-Based Representation for Deep Reinforcement Learning. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), 2023.

• L. Yuan, Z.-Q. Zhang, K. Xue, H. Yin, F. Chen, C. Guan, L.-H. Li, C. Qian, and Y. Yu. Robust Multi-agent Coordination via Evolutionary Generation of Auxiliary Adversarial Attackers. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), 2023.

• Y.-R. Gu, C. Bian, and C. Qian. Submodular Maximization Under the Intersection of Matroid and Knapsack Constraints. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), 2023.

• F. Chen, C.-H. Wang, F.-X. Zhang, H. Ding, Q.-Y. Zhong, S.-L. Pu, and Z.-Z. Zhang. Towards Deployment-Efficient and Collision-Free Multi-Agent Path Finding (Student Abstract). In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), 2023.

• L. Li, B.-W. Tao, L. Han, D.-C. Zhan, and H.-J. Ye. Twice Class Bias Correction for Imbalanced Semi-Supervised Learning. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), 2023.

• A.-R. Wang, H.-Y. Yang, F. Mao, Z.-Z. Zhang, Y. Yu, and X.-Y. Liu. Anti-Drifting Feature Selection via Deep Reinforcement Learning (Student Abstract). In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), 2023.

• Y.-B. Wang, Y.-Y. Wan, S.-M. Zhang, and L.-J. Zhang. Distributed Projection-free Online Learning for Smooth and Convex Losses. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), 2023.

• Z. Xie, H. Sun, and M. Li. Semi-Supervised Learning with Support Isolation by Small-Paced Self-Training. In: Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI'23), 2023.

AAMAS

• X.-H. Liu, F. Xu, X.-Y. Zhang, T.-Y. Liu, S.-Y. Jiang, R.-F. Chen, Z.-Z. Zhang, and Y. Yu. How To Guide Your Learner: Imitation Learning with Active Adaptive Expert Involvement. In: Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS'23), 2023.

• S.-W. Zhang, J.-H. Cao, L. Yuan, Y. Yu, and D.-C. Zhan. Self-Motivated Multi-Agent Exploration. In: Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS'23), 2023.

AISTATS

• J.-W. Shan, P. Zhao, and Z.-H. Zhou. Beyond Performative Prediction: Open-environment Learning with Presence of Corruptions. In: Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS'23), 2023.

• Q.-C. Zheng, S.-H. Lyu, S.-Q. Zhang, Y. Jiang, and Z.-H. Zhou. On the Consistency Rate of Decision Tree Learning Algorithms. In: Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS'23), 2023.

• J. Wang, P. Zhao, and Z.-H. Zhou. Revisiting Weighted Strategy for Non-stationary Parametric Bandits. In: Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS'23), 2023.

CEC

• F. Neumann, A. Neumann, C. Qian, V.-A. Do, J. de Nobel, D. Vermetten, S. S. Ahouei, F.-R. Ye, H. Wang, and T. Bäck. Benchmarking Algorithms for Submodular Optimization Problems Using IOHProfiler. In: 2023 IEEE Congress on Evolutionary Computation (CEC'23), 2023.

CIKM

• Y.-X. Sun, Y.-L. Zhang, W. Wang, L.-F. Li, and J. Zhou. Treatment effect estimation across domains. In: Proceedings of the 32nd ACM International Conference on Information and Knowledge Management (CIKM'23), 2023.

CVPR

• Y.-K. Zhang, Q.-W. Wang, D.-C. Zhan, and H.-J. Ye. Learning Debiased Representations via Conditional Attribute Interpolation. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'23), 2023.

• Y.-H. Cao, P.-Q. Sun, and S.-C. Zhou. Three Guidelines You Should Know for Universally Slimmable Self-Supervised Learning. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'23), 2023.

DSAA

• C.-Y. Cai, W. Wang, and Y. Jiang. Adaptive clustered federated learning with representation similarity. In: Proceedings of the 10th IEEE International Conference on Data Science and Advanced Analytics (DSAA'23), 2023.

ECAI

• Y. Xie, Z.-H. Tan, Y. Jiang, and Z.-H. Zhou. Identifying Helpful Learnwares without Examining the Whole Market. In: Proceedings of the 26th European Conference on Artificial Intelligence (ECAI'23), 2023.

ICDM

• Y.-L. Du, Y.-F. Ma, Z. Xie, and M. Li. Beyond Lexical Consistency: Preserving Semantic Consistency for Program Translation. In: Proceedings of the 23th IEEE International Conference on Data Mining (ICDM'23), 2023.

• Z.-J. Wang, Y. Zhu, and K. M. Ting. Distribution-Based Trajectory Clustering. In: Proceedings of the 23th IEEE International Conference on Data Mining (ICDM'23), 2023.

ICLR

• D.-W. Zhou, Q.-W. Wang, H.-J. Ye, and D.-C. Zhan. A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning. In: Proceedings of the 11th International Conference on Learning Representations (ICLR'23), 2023.

• L. Han, H.-J. Ye, and D.-C. Zhan. Augmentation Component Analysis: Modeling Similarity via the Augmentation Overlaps. In: Proceedings of the 11th International Conference on Learning Representations (ICLR'23), 2023.

• F.-X. Zhang, C.-X. Jia, Y.-C. Li, L. Yuan, Y. Yu, and Z.-Z. Zhang. Discovering Generalizable Multi-agent Coordination Skills from Multi-task Offline Data. In: Proceedings of the 11th International Conference on Learning Representations (ICLR'23), 2023.

• S. Wu, J. Yao, H.-B. Fu, Y. Tian, C. Qian, Y.-D. Yang, Q. Fu, and Y. Wei. Quality-Similar Diversity via Population Based Reinforcement Learning. In: Proceedings of the 11th International Conference on Learning Representations (ICLR'23), 2023.

ICML

• Y.-H. Yan, P. Zhao, and Z.-H. Zhou. Fast Rates in Time-Varying Strongly Monotone Games. In: International Conference on Machine Learning (ICML'23), 2023.

• Y.-H. Ran, Y.-C. Li, F.-X. Zhang, Z.-Z. Zhang, and Y. Yu. Policy Regularization with Dataset Constraint for Offline Reinforcement Learning. In: International Conference on Machine Learning (ICML'23), 2023.

• G.-Q. Liu, D. Xue, S.-F. Xie, Y.-C. Xia, A. Tripp, K. Maziarz, M. Segler, T. Qin, Z.-Z. Zhang, and T.-Y. Liu. Retrosynthetic Planning with Dual Value Networks In: Proceedings of the 40th International Conference on Machine Learning. In: International Conference on Machine Learning (ICML'23), 2023.

• L.-H. Jia, L.-Z. Guo, Z. Zhou, J.-J. Shao, Y.-K. Xiang, and Y.-F. Li. Bidirectional Adaptation for Robust Semi-Supervised Learning with Inconsistent Data Distributions. In: International Conference on Machine Learning (ICML'23), 2023.

• L.-Z. Guo, Z. Zhou,Y.-F. Li, and Z.-H. Zhou. Identifying Useful Learnwares for Heterogeneous Label Spaces. In: International Conference on Machine Learning (ICML'23), 2023.

• W. Jiang, J.-Y. Qin, L.-Y. Wu, C.-Y. Chen, T.-B. Yang, and L.-J. Zhang. Learning Unnormalized Statistical Models via Compositional Optimization. In: International Conference on Machine Learning (ICML'23), 2023.

• Z.-H. Qiu, Q. Hu, Z.-N. Yuan, D. Zhou, L.-J. Zhang, and T.-B. Yang. Not All Semantics are Created Equal: Contrastive Self-supervised Learning with Automatic Temperature Individualization. In: International Conference on Machine Learning (ICML'23), 2023.

• Z. Zhou, L.-Z. Guo, L.-H. Jia, D.-C. Zhang, and Y.-F. Li. ODS: Test-Time Adaptation in the Presence of Open-World Data Shift. In: International Conference on Machine Learning (ICML'23), 2023.

• S.-H. Wan, Y.-C. Wang, M.-H. Shao, R.-Y. Chen, and D.-C. Zhan. SeMAIL: Eliminating Distractors in Visual Imitation via Separated Models. In: International Conference on Machine Learning (ICML'23), 2023.

• H. Zhang, K.-F. Zhang, K. M. Ting, and Y. Zhu. Towards a Persistence Diagram that is Robust to Noise and Varied Densities. In: International Conference on Machine Learning (ICML'23), 2023.

IJCAI

• Y.-X. Huang, Z.-Q. Sun, G.-Y. Li, X.-B. Tian, W.-Z. Dai, W. Hu, Y. Jiang, and Z.-H. Zhou. Enabling Abductive Learning to Exploit Knowledge Graph. In: Proceedings of the 32th International Joint Conference on Artificial Intelligence (IJCAI'23), 2023.

• P. Tan, Z.-H. Tan, Y. Jiang, and Z.-H. Zhou. Handling Learnwares Developed from Heterogeneous Feature Spaces without Auxiliary Data. In: Proceedings of the 32th International Joint Conference on Artificial Intelligence (IJCAI'23), 2023.

• R.-J. Wang, K. Xue, H.-P. Shang, C. Qian, H.-B. Fu, and Q. Fu. Multi-objective Optimization-based Selection for Quality-Diversity by Non-surrounded-dominated Sorting. In: Proceedings of the 32th International Joint Conference on Artificial Intelligence (IJCAI'23), 2023.

• C. Bian, Y.-W. Zhou, M.-Q. Li, and C. Qian. Stochastic Population Update Can Provably Be Helpful in Multi-Objective Evolutionary Algorithms. In: Proceedings of the 32th International Joint Conference on Artificial Intelligence (IJCAI'23), 2023.

• Y.-F. Ma, Y.-L. Du, and M. Li. Capturing the Long-Distance Dependency in the Control Flow Graph via Structural-Guided Attention for Bug Localization. In: Proceedings of the 32th International Joint Conference on Artificial Intelligence (IJCAI'23), 2023.

KDD

• J.-Q. Yang, Y.-C. Xu, J.-L. Shen, K.-B. Fan, D.-C. Zhan, and Y. Yang. IDToolkit: A Toolkit for Benchmarking and Developing Inverse Design Algorithms in Nanophotonics. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'23), 2023.

• J.-C. Xu, C. Chen, F. Zhang, L. Yuan, Z.-Z. Zhang, and Y. Yu. Internal Logical Induction for Pixel-Symbolic Reinforcement Learning. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'23), 2023.

MICCAI

• Y. Shi, R.-X. Li, W.-Q. Shao, X.-C. Duan, H.-J. Ye, D.-C. Zhan, B.-S. Pan, B.-L. Wang, W. Guo, and Y. Jiang. A Multi-Task Method for Immunofixation Electrophoresis Image Classification. In: Proceedings of the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'23), 2023.

NeurIPS

• J.-Q. Yang, D.-C. Zhan, and L. Gan. Beyond Probability Partitions: Calibrating Neural Networks with Semantic Aware Grouping. In: Advances in Neural Information Processing Systems 36 (NeurIPS'23), 2023.

• J.-H. Wu, S.-Q. Zhang, Y. Jiang, and Z.-H. Zhou. Complex-valued Neurons Can Learn More but Slower than Real-valued Neurons via Gradient Descent. In: Advances in Neural Information Processing Systems 36 (NeurIPS'23), 2023.

• L.-F. Li, P. Zhao, and Z.-H. Zhou. Dynamic Regret of Adversarial Linear Mixture MDPs. In: Advances in Neural Information Processing Systems 36 (NeurIPS'23), 2023.

• Q.-W. Wang, D.-W. Zhou, Y.-K. Zhang, D.-C. Zhan, and H.-J. Ye. Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration. In: Advances in Neural Information Processing Systems 36 (NeurIPS'23), 2023.

• Y.-Q. Shi, K. Xue, L. Song, and C. Qian. Macro Placement by Wire-Mask-Guided Black-Box Optimization. In: Advances in Neural Information Processing Systems 36 (NeurIPS'23), 2023.

• Y.-K. Zhang, T.-J. Huang, Y.-X. Ding, D.-C. Zhan, and H.-J. Ye. Model Spider: Learning to Rank Pre-Trained Models Efficiently. In: Advances in Neural Information Processing Systems 36 (NeurIPS'23), 2023.

• T. Qin, T.-Z. Wang, and Z.-H. Zhou. Rehearsal Learning for Avoiding Undesired Future. In: Advances in Neural Information Processing Systems 36 (NeurIPS'23), 2023.

• Y.-H. Yan, P. Zhao, and Z.-H. Zhou. Universal Online Learning with Gradient Variations: A Multi-layer Online Ensemble Approach. In: Advances in Neural Information Processing Systems 36 (NeurIPS'23), 2023.

• J.-X. Shi, T. Wei, Y.-K. Xiang, and Y.-F. Li. How Re-sampling Helps for Long-Tail Learning?. In: Advances in Neural Information Processing Systems 36 (NeurIPS'23), 2023.

• Z.-J. Zhou, J. Ni, J.-H. Yao, and W. Gao. On the Exploration of Local Significant Differences for Two-Sample Test. In: Advances in Neural Information Processing Systems 36 (NeurIPS'23), 2023.

• X.-R. Xie, M.-J. Yuan, X.-T. Bai ,W. Gao and Z.-H. Zhou. On the Gini-impurity Preservation For Privacy Random Forests. In: Advances in Neural Information Processing Systems 36 (NeurIPS'23), 2023.

• L. Zhang, J.-F. Li, and W. Wang. Semi-supervised domain generalization with known and unknown classes. In: Advances in Neural Information Processing Systems 36 (NeurIPS'23), 2023.

• R.-J. Wang, K. Xue, Y.-T. Wang, P. Yang, H.-B. Fu, Q. Fu, and C. Qian. Diversity from Human Feedback. In: Advances in Neural Information Processing Systems 36 (NeurIPS'23 Workshop on Agent Learning in Open-Endedness), 2023.

SDM

• T. Qin, T.-Z. Wang, and Z.-H. Zhou. Learning Causal Structure on Mixed Data with Tree-Structured Functional Models. In: Proceedings of the 2023 SIAM International Conference on Data Mining (SDM'23), 2023.

• Z. Zhuang, K. M. Ting, G.-S. Pang, and S.-B. Song. Subgraph Centralization: A Necessary Step for Graph Anomaly Detection. In: Proceedings of the 2023 SIAM International Conference on Data Mining (SDM'23), 2023.

UAI

• Z.-Q. Zhang, L. Yuan, L.-H. Li, K. Xue, C.-X. Jia, C. Guan, C. Qian, and Y. Yu. Fast Teammate Adaptation in the Presence of Sudden Policy Change. In: Proceedings of the 39th Conference on Uncertainty in Artificial Intelligence (UAI'23), 2023.

WSDM

• Y.-L. Zhang, Y.-X. Sun, F.-F. Fan, M. Li, Y.-Y. Zhao, W. Wang, L.-F. Li, J. Zhou, and J.-H. Feng. A framework for detecting frauds from extremely few labels. In: Proceedings of the 16th ACM International Conference on Web Search and Data Mining (WSDM'23), 2023.

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Journal Article and Book

[A] [C] [D] [F] [I] [J] [M] [S] [T] [计] [软] [中]

ACS Photonics

• J.-Q. Yang, Y.-C. Xu, K.-B. Fan, J.-B. Wu, C.-H. Zhang, D.-C. Zhan, B.-B. Jin, and W. J. Padilla. Normalizing Flows for Efficient Inverse Design of Thermophotovoltaic Emitters. In: ACS Photonics, 2023, 10(4): 1001–1011.

Chaos

• H.-M. Bai, W.-Y. Xu, S.-F. Yang, and J.-D. Cao. Distributed inertial online game algorithm for tracking generalized Nash equilibria. In: Chaos, 2023, 33(10).

Data Mining and Knowledge Discovery

• X.-C. Li, Y. Yang, and D.-C. Zhan. MrTF: Model Refinery for Transductive Federated Learning. In: Data Mining and Knowledge Discovery, 2023, 37(5): 2046-2069.

Frontiers of Computer Science

• Z.-Z. Zhang. Communication-Robust Multi-Agent Learning by Adaptable Auxiliary Multi-Agent Adversary Generation. In: Frontiers of Computer Science, 2023, in press.

• C.-X. Jia, F.-X. Zhang, T. Xu, J.-C. Pang, Z.-Z. Zhang, and Y. Yu. Model Gradient: Unified Model and Policy Learning in Model-based Reinforcement Learning. In: Frontiers of Computer Science, 2023, in press.

• Y. Shi, H.-J. Ye, D.-L. Man, X.-X. Han, D.-C. Zhan, and Y. Jiang. Revisiting Multi-dimensional Classification from a Dimension-wise Perspective. In: Frontiers of Computer Science, 2023, in press.

• Z. Zhou, Y.-X. Jin, and Y.-F. Li. RTS: Learning Robustly from Time Series Data with Noisy Label. In: Frontiers of Computer Science, 2023, in press.

IEEE Transactions on Pattern Analysis and Machine Intelligence

• Y.-H. Cao and J.-X. Wu. Tobias: A Random CNN Sees Objects. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, in press.

Intelligent Computing

• C.-Y. Wu and Z.-Z. Zhang. Surfing Information: The Challenge of Intelligent Decision-Making. In: Intelligent Computing, 2023, 2(0041).

Journal of Computer Science and Technology

• Y.-H. Cao and J.-X. Wu. Random Subspace Sampling for Classification with Missing Data. In: Journal of Computer Science and Technology, 2023, in press.

Machine Learning

• Y.-X. He, Y.-C. Wu, C. Qian, and Z.-H. Zhou. Margin Distribution and Structural Diversity Guided Ensemble Pruning. In: Machine Learning, 2023, in press.

• T. Qin, L.-F. Li, T.-Z. Wang, and Z.-H. Zhou. Tracking Treatment Effect Heterogeneity in Evolving Environments. In: Machine Learning, 2023, in press.

Science China: Information Sciences

• L.-H. Jia, L.-Z. Guo, Z. Zhou, and Y.-F. Li. LAMDA-SSL: A Comprehensive Semi-Supervised Learning Toolkit. In: Science China: Information Sciences, 2023, in press.

• R.-J. Qin, F. Chen, T.-H. Wang, L. Yuan, X.-R. Wu, Y.-P. Kang, Z.-Z. Zhang, C.-J. Zhang, and Y. Yu. Multi-Agent Policy Transfer via Task Relationship Modeling. In: Science China: Information Sciences, 2023, in press.

• J.-X. Shi, T. Wei, and Y.-F. Li. Residual Diverse Ensemble for Long-Tailed Multi-Label Text Classification. In: Science China: Information Sciences, 2023, in press.

• L. Yuan, T. Jiang, L.-H. Li, F. Chen, Z.-Z. Zhang, and Y. Yu. Robust Cooperative Multi-agent Reinforcement Learning via Multi-view Message Certification. In: Science China: Information Sciences, 2023, in press.

Theoretical Computer Science

• C. Qian, D.-X. Liu, C. Feng, and K. Tang. Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences. In: Theoretical Computer Science, 2023, in press.

Transactions on Machine Learning Research

• L. Han, H.-J. Ye, and D.-C. Zhan. On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning. In: Transactions on Machine Learning Research, 2023, in press.

计算机研究与发展

• 黄宇轩, 姜远. 带拒绝推理的反绎学习方法. In: 计算机研究与发展, 2023, in press.

软件学报

• Z. Zhou, D.-C. Zhang,Y.-F. Li, and M.-L. Zhang. Towards Robust Test-Time Adaptation for Open-Set Recognition. In: 软件学报, 2023, in press.

中国科学

• L.-Z. Guo and Y.-F. Li. 稳健选择伪标注的混合式半监督学习. In: 中国科学, 2023, in press.

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