[Conference Paper][Journal Article]

Conference Paper

[AAAI] [AAMAS] [ACL] [CIKM] [CVPR] [DAC] [DATE] [ECML] [EMNLP] [GECCO] [ICCV] [ICLR] [ICML] [IJCAI] [KDD] [KSEM] [NeurIPS] [PRCV]

AAAI

• J. Zhou, K. Zhu, and J. Wu. All You Need in Knowledge Distillation Is a Tailored Coordinate System. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25), 2025.

• Y.-K. Zhang, D.-C. Zhan, and H.-J. Ye. Capability Instruction Tuning. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25), 2025.

• W.-C. Hu, W.-Z. Dai, Y. Jiang, and Z.-H. Zhou. Efficient Rectification of Neuro-Symbolic Reasoning Inconsistencies by Abductive Reflection. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25), 2025.

• Z. Zhou, K.-Y. Yu, L.-Z. Guo, and Y.-F. Li. Fully Test-time Adaptation for Tabular Data. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25), 2025.

• T. Qin, T.-Z. Wang, and Z.-H. Zhou. Gradient-Based Nonlinear Rehearsal Learning with Multivariate Alterations. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25), 2025.

• H.-L. Sun, D.-W. Zhou, H. Zhao, L. Gan, D.-C. Zhan, and H.-J. Ye. MOS: Model Surgery for Pre-Trained Model-Based Class-Incremental Learning. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25), 2025.

• S. Yang, Y. Wan, and L. Zhang. Online Nonsubmodular Optimization with Delayed Feedback in the Bandit Setting. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25), 2025.

• Y. Wang, Y. Wan, and L. Zhang. Revisiting Projection-Free Online Learning with Time-Varying Constraints. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25), 2025.

• R.-C. Rao, D.-W. Li, and M. Li. Slice-and-Pack: Tailoring Deep Models for Customized Requirements. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25), 2025.

• Y. Wang, Y. Jian, W. Yang, S. Lu, L. Shen, B. Wang, X. Zeng, and L. Zhang. Towards Unbiased Information Extraction and Adaptation in Cross-Domain Recommendation. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25), 2025.

• H. Yu, Y. Zhou, B. Chen, Z. Yang, S. Li, Y. Li, and J. Wu. Treasures in Discarded Weights for LLM Quantization. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence (AAAI'25), 2025.

AAMAS

• P. Wang, J.-C. Pang, W. Chenyang, X.-H. Liu, T.-S. Liu, S.-H. Yang, H. Qian, and Y. Yu. InCLET: In-context Learning from Language Models can Improve Embodied Instruction-following. In: Proceedings of the 24th International Conference on Autonomous Agents and Multiagent Systems (AAMAS'25), 2025.

ACL

• W.-S. Fan, S. Lu, S.-Y. Xing, X.-C. Li, and D.-C. Zhan. Maximizing the Effectiveness of Larger BERT Models for Compression. In: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL'25), 2025.

• H.-L. Sun, Z. Sun, H. Peng, and H.-J. Ye. Mitigating Visual Forgetting via Take-along Visual Conditioning for Multi-modal Long CoT Reasoning. In: Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (ACL'25), 2025.

CIKM

• Y. Xu, Z. Yang, and K. M. Ting. Contrastive Multi-View Graph Hashing. In: Proceedings of the 34th ACM International Conference on Information and Knowledge Management (CIKM'25), 2025.

CVPR

• D. Zhou, Z.-W. Cai, H.-J. Ye, L. Zhang, and D.-C. Zhan. Dual consolidation for pre-trained model-based domain-incremental learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'25), 2025.

• N. Tang, M. Fu, and J. Wu. Minimal Interaction Separated Tuning: A New Paradigm for Visual Adaptation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'25), 2025.

• M. Fu, H. Yu, J. Shao, J. Zhou, K. Zhu, and J. Wu. Quantization without Tears. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'25), 2025.

• H. Hao, J. Han, C. Li, Y.-F. Li, and X. Yue. Retrieval-Augmented Personalization for Multimodal Large Language Models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'25), 2025.

• B. Zheng, D.-W. Zhou, H.-J. Ye, and D.-C. Zhan. Task-Agnostic Guided Feature Expansion for Class-Incremental Learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'25), 2025.

DAC

• Y. Shi, X. Lin, S. Xu, S. Kai, K. Xue, M. Yuan, C. Qian, and Z.-H. Zhou. ReMaP: Macro Placement by Recursively Prototyping and Periphery-Guided Relocating. In: Proceedings of the 62nd ACM/IEEE Design Automation Conference (DAC'25), 2025.

DATE

• Y. Shi, S. Xu, S. Kai, X. Lin, K. Xue, M. Yuan, and C. Qian. Timing-Driven Global Placement by Efficient Critical Path Extraction. In: Proceedings of the 2025 Design, Automation & Test in Europe Conference & Exhibition (DATE'25), 2025.

ECML/PKDD

• H. Zhang, and K. M. Ting. Machine Unlearning for Random Forest via Method of Images. In: Proceedings of the 36th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'25), 2025.

• Y. Xu, and K. M. Ting. Voronoi Diagram Encoded Hashing. In: Proceedings of the 36th European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'25), 2025.

EMNLP

• D.-C. Zhang, Y. Zhao, J. Wu, L. Zhang, B. Li, W. Yin, Y. Jiang, Y.-F. Li, K. Tu, P. Xie, and F. Huang. EvolveSearch: An Iterative Self-Evolving Search Agent. In: Proceedings of the 30th Conference on Empirical Methods in Natural Language Processing (EMNLP'25), 2025.

• S.-Y. Tian, Z. Zhou, K.-Y. Yu, M. Yang, L.-H. Jia, L.-Z. Guo, and Y.-F. Li. VCSearch: Bridging the Gap Between Well-Defined and Ill-Defined Problems in Mathematical Reasoning. In: Proceedings of the 30th Conference on Empirical Methods in Natural Language Processing (EMNLP'25), 2025.

• D.-C. Zhang, X. Zhang, Y. Fei, R. Hu, X.-W. Yang, Z. Zhou, B. Li, Y.-F. Li, X. Shi, and W. Lin. AutoEvolve: Automatically Evolving Queries for Applicable and Scalable Retrieval-Augmented Generation Benchmarking. In: Proceedings of the 30th Conference on Empirical Methods in Natural Language Processing (EMNLP'25), 2025.

• N. Jiang, Z. Wu, D.-C Zhan, F. Lai, and S. Lian. DART: Distilling Autoregressive Reasoning to Silent Thought. In: Proceedings of the 30th Conference on Empirical Methods in Natural Language Processing (EMNLP'25), 2025.

GECCO

• X. He, H.-P. Shang, and C. Qian. How to Train Algorithm Selection Models: Insights from Black-box Continuous Optimization. In: 15th Workshop on Evolutionary Computation for the Automated Design of Algorithms at GECCO'25, 2025.

ICCV

• D.-W. Zhou, K.-W. Li, J. Ning, H.-J. Ye, L. Zhang, and D.-C. Zhan. External Knowledge Injection for CLIP-Based Class-Incremental Learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV'25), 2025.

• J. Shao, H. Zhang, H. Yu, and J. Wu. Memory-Efficient Generative Models via Product Quantization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV'25), 2025.

ICLR

• H. Lin, Y.-Y. Xu, Y. Sun, Z. Zhang, Y.-C. Li, C. Jia, J. Ye, J. Zhang, and Y. Yu. Any-step Dynamics Model Improves Future Predictions for Online and Offline Reinforcement Learning. In: Proceedings of the 13th International Conference on Learning Representations (ICLR'25), 2025.

• X.-W. Yang, Z. Zhou, H. Wang, A. Li, W.-D. Wei, H. Jin, Z. Li, and Y.-F. Li. CARTS: Advancing Neural Theorem Proving with Diversified Tactic Calibration and Bias-Resistant Tree Search. In: Proceedings of the 13th International Conference on Learning Representations (ICLR'25), 2025.

• L. Yuan, Y. Bian, L. Li, Z. Zhang, C. Guan, and Y. Yu. Efficient Multi-agent Offline Coordination via Diffusion-based Trajectory Stitching. In: Proceedings of the 13th International Conference on Learning Representations (ICLR'25), 2025.

• J.-C. Pang, N. Tang, K. Li, Y. Tang, X.-Q. Cai, Z.-Y. Zhang, G. Niu, M. Sugiyama, and Y. Yu. Learning View-invariant World Models for Visual Robotic Manipulation. In: Proceedings of the 13th International Conference on Learning Representations (ICLR'25), 2025.

• C. Jiang, X. Shu, H. Qian, X. Lu, J. Zhou, A. Zhou, and Y. Yu. LLMOPT: Learning to Define and Solve General Optimization Problems from Scratch. In: Proceedings of the 13th International Conference on Learning Representations (ICLR'25), 2025.

• R.-X. Tan, K. Xue, S.-H., Lyu, H. Shang, Y. Wang, Y. Wang, S. Fu, and C. Qian. Offline Model-Based Optimization by Learning to Rank. In: Proceedings of the 13th International Conference on Learning Representations (ICLR'25), 2025.

• X.-H. Liu, Y. Du, J. Wang, and Y. Yu. On the Optimization Landscape of Low Rank Adaptation Methods for Large Language Models. In: Proceedings of the 13th International Conference on Learning Representations (ICLR'25), 2025.

• Y.-C. Li, F. Zhang, W. Qiu, L. Yuan, C. Jia, Z. Zhang, Y. Yu, and B. An. Q-Adapter: Customizing Pre-trained LLMs to New Preferences with Forgetting Mitigation. In: Proceedings of the 13th International Conference on Learning Representations (ICLR'25), 2025.

• T.-S. Liu, X.-H. Liu, R. Chen, L. Jin, P. Wang, Z. Zhang, and Y. Yu. Semantic Temporal Abstraction via Vision-Language Model Guidance for Efficient Reinforcement Learning. In: Proceedings of the 13th International Conference on Learning Representations (ICLR'25), 2025.

• H. Qian, Y. Zhu, X. Shu, X. An, Y. Wen, S. Liu, H. Lu, A. Zhou, K. Tang, and Y. Yu. SOO-Bench: Benchmarks for Evaluating the Stability of Offline Black-Box Optimization. In: Proceedings of the 13th International Conference on Learning Representations (ICLR'25), 2025.

• Y.-K. Zhang, S. Lu, Q.-G. Chen, D.-C. Zhan, and H.-J. Ye. ZooProbe: A Data Engine for Evaluating, Exploring, and Evolving Large-scale Training Data for Multimodal LLMs. In: Proceedings of the 13th International Conference on Learning Representations (ICLR'25), 2025.

• H.-J. Ye, H.-H. Yin, D.-C. Zhan, and W.-L. Chao. Revisiting Nearest Neighbor for Tabular Data: A Deep Tabular Baseline Two Decades Later. In: Proceedings of the 13th International Conference on Learning Representations (ICLR'25), 2025.

ICML

• L. Li, D.-W. Zhou, H.-J. Ye, and D.-C.Zhan. Addressing Imbalanced Domain-Incremental Learning through Dual-Balance Collaborative Experts. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• C.-X. Gao, C. Wu, M. Cao, C. Xiao, Y. Yu, and Z. Zhang. Behavior-Regularized Diffusion Policy Optimization for Offline Reinforcement Learning. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• C. Jia, Z. Li, Y.-C. Li, P. Wang, Z. Hou, Y. Dong, and Y. Yu. Controlling Large Language Model with Latent Action. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• S. Yang, Y. Wan, P. Li, Y. Wang, X. Zhang, Z. Wei, and L. Zhang. Dimension-Free Adaptive Subgradient Methods with Frequent Directions. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• D.-X. Liu, and C. Qian. Improved Theoretically-Grounded Evolutionary Algorithms for Subset Selection with a Linear Cost Constraint. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• Z. Zhang, T. Xu, X. Du, X. Cao, Y. Sun, and Y. Yu. Improving Reward Model Generalization from Adversarial Process Enhanced Preferences. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• Z. Zhang, B. Yang, L. Li, Y. Bian, R. Xue, F. Chen, Y.-C. Li, L. Yuan, and Y. Yu. Learning to Reuse Policies in State Evolvable Environments. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• L. Li, L. Yuan, P. Liu, T. Jiang, and Y. Yu. LLM-Assisted Semantically Diverse Teammate Generation for Efficient Multi-agent. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• C.-R. Gao, H.-P. Shang, K. Xue, and C. Qian. Neural Solver Selection for Combinatorial Optimization. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• J.-Q. Guo, M.-Z. Qian, W. Gao, and Z.-H. Zhou. On the diversity of adversarial ensemble learning. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• H.-L. Sun, D.-W. Zhou, Y. Li, S. Lu, C. Yi, Q.-G. Chen, Z. Xu, W. Luo, K. Zhang, D.-C. Zhan, and H.-J. Ye. Parrot: Multilingual visual instruction tuning. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• H.-Z. Tan, Z. Zhou, L.-Z. Guo, and Y.-F. Li. Pre-Trained Vision-Language Model Selection and Reuse for Downstream Tasks. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• M. Li, H. Qian, T.-Z. Wang, ShujunLi, M. Zhang, and A. Zhou. Strong and weak identifiability of optimization-based causal discovery. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• Z.-J. Cheng, Z.-Y. Jia, Z. Zhou, L.-Z. Guo, and Y.-F. Li. TabFSBench: Tabular Benchmark for Feature Shifts in Open Environment. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• S.-Y. Liu, and H.-J. Ye. TabPFN Unleashed: A Scalable and Effective Solution to Tabular Classification Problems. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• R.-X. Tan, M. Chen, K. Xue, Y. Wang, Y. Wang, S. Fu, and C. Qian. Towards Universal Offline Black-Box Optimization via Learning Language Model Embeddings. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• H.-R. Cai, and H.-J. Ye. Understanding the Limits of Deep Tabular Methods with Temporal Shift. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• L.-H. Jia, W.-C. Hu, J.-J. Shao, L.-Z. Guo, and Y.-F. Li. Verification Learning: Make Unsupervised Neuro-Symbolic System Feasible. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• J.-P. Jiang, T. Zhou, D.-C. Zhan, H.-J. Y. D.-C. Zhan, and H.-J. Ye. Compositional Condition Question Answering in Tabular Understanding. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• W.-B. Du, H.-Y. Lei, L. Tao, T.-Z. Wang, and Z.-H. Zhou. Enabling optimal decisions in rehearsal learning under CARE condition. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• Y. Wang, R. Yu, S. Wan, L. Gan, and D.-C. Zhan. Founder: Grounding foundation models in world models for open-ended embodied decision making. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• J. Wang, Y.-J. Zhang, P. Zhao, and Z.-H. Zhou. Heavy-Tailed Linear Bandits: Huber Regression with One-Pass Update. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• T.-J. Huang, J.-Q. Yang, C. Shen, K.-Q. Liu, D.-C. Zhan, H.-J. Y.-C. Zhan, and H.-J. Ye. Improving LLMs for Recommendation with Out-Of-Vocabulary Tokens. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• T.-Z. Wang, W.-B. Du, and Z.-H. Zhou. Polynomial-delay MAG listing with novel locally complete orientation rules. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

• Y.-Y. Qian, Y.-Z. Xu, Z.-Y. Zhang, P. Zhao, and Z.-H. Zhou. TreeLoRA: Efficient Continual Learning via Layer-Wise LoRAs Guided by a Hierarchical Gradient-Similarity Tree. In: Proceedings of the 42nd International Conference on Machine Learning (ICML'25), 2025.

IJCAI

• S. Ren, Z. Liang, M. Li, and C. Qian. A Theoretical Perspective on Why Stochastic Population Update Needs an Archive in Evolutionary Multi-objective Optimization. In: Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI'25), 2025.

• L. Tao, T.-Z. Wang, Y. Jiang, and Z.-H. Zhou. Avoiding Undesired Future with Sequential Decisions. In: Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI'25), 2025.

• W.-C. Hu, W.-Z. Dai, Y. Jiang, and Z.-H. Zhou. Efficient Rectification of Neuro-Symbolic Reasoning Inconsistencies by Abductive Reflection. In: Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI 2025 Best Papers from Sister Conferences Track), 2025.

• W.-J. Zhou, S. Ding, Z.-L. Li, and W. Wang. Enhancing the performance of global model by improving the adaptability of local models in federated learning. In: Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI'25), 2025.

• J.-D. Liu, Z.-H. Tan, and Z.-H. Zhou. Identifying and Reusing Learnwares across Different Label Spaces. In: Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI'25), 2025.

• X.-W. Yang, J.-J. Shao, L.-Z. Guo, B.-W. Zhang, Z. Zhou, L.-H. Jia, W.-Z. Dai, and Y.-F. Li. Neuro-Symbolic Artificial Intelligence: Towards Improving the Reasoning Abilities of Large Language Models. In: Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI'25), 2025.

• F. Xu, W.-Y. Chen, and W. Gao. On the Learning with Augmented Class via Forests. In: Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI'25), 2025.

• R. Yu, S. Wan, Y. Wang, C.-X. Gao, L. Gan, Z.-Z Zhang, and D.-C.Zhan. Reward Models in Deep Reinforcement Learning: A Survey. In: Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI'25), 2025.

• S. Yang, W. Yang, W. Jiang, Y. Wan, and L. Zhang. Smoothed Online Convex Optimization with Delayed Feedback. In: Proceedings of the 34th International Joint Conference on Artificial Intelligence (IJCAI'25), 2025.

KDD

• J.-J. Shao, H.-R. Hao, X.-W. Yang, and Y.-F. Li. Abductive Learning for Neuro-Symbolic Grounded Imitation. In: Proceedings of the 31st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'25), 2025.

• L. Ma, Y.-X. Ding, Z.-Y. Zhang, and Z.-H. Zhou. Achieving Nearly-Optimal Regret and Sample Complexity in Dueling Bandits with Applications in Online Recommendations. In: Proceedings of the 31st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'25), 2025.

• Y.-Y. Qian, Y.-H. Wang, Z.-Y. Zhang, Y. Jiang, and Z.-H. Zhou. Adapting to Generalized Online Label Shift by Invariant Representation Learning. In: Proceedings of the 31st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'25), 2025.

• J.-D. Liu, Z.-H. Tan, and Z.-H. Zhou. Dynamic Learnware Filtering for Efficient Learnware Identification and System Slimming. In: Proceedings of the 31st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD'25), 2025.

KSEM

• Y. Ma, Y. Zhu, Y. Xu, and K. M. Ting. Streaming Hierarchical Clustering for Emerging New Class. In: Proceedings of The 18th International Conference on Knowledge Science, Engineering and Management (KSEM'25), 2025.

NeurIPS

• H.-J. Ye, S.-Y. Liu, and W.-L. Chao. A Closer Look at TabPFN v2: Strength, Limitation, and Extension. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• H.-Y. He, and M. Li. A learnability analysis on neuro-symbolic learning. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• Z. Zhou, Y. Tan, Z. Li, Y. Yao, L.-Z. Guo, Y.-F. Li, and X. Ma. A Theoretical Study on Bridging Internal Probability and Self-Consistency for LLM Reasoning. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• R. Xue, Z. Zhang, L. Li, C. Guan, L. Yuan, and Y. Yu. Adaptable safe policy learning from multi-task data with constraint prioritized decision transformer. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• Q. Cheng, Y. Wan, L. Wu, C. Hou, and L. Zhang. Continual Subspace Optimization for Continual Learning. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• W.-C. Hu, Q.-J. Li, L.-H. Jia, C. Ge, Y.-F. Li, Y. Jiang, and Z.-H. Zhou. Curriculum Abductive Learning. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• E.-H. Gao, C. Ge, Y. Jiang, and Z.-H. Zhou. Discovering Symbolic Partial Differential Equation by Abductive Learning. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• H.-R. Cai, and H.-J. Ye. Feature-aware Modulation for Learning from Temporal Tabular Data. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• J. Wang, C. Chen, J. Xu, Z. Zhang, and Y. Yu. Focus-then-reuse: Fast adaptation in visual perturbation environments. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• Y. Zhao, Y.-H. Yan, K. Y. Levy, and P. Zhao. Gradient-Variation Online Adaptivity for Accelerated Optimization with Hölder Smoothness. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• Z.-K. Chen, J.-P. Jiang, H.-J. Ye, and D.-C.Zhan. Hawk: Leveraging Spatial Context for Faster Autoregressive Text-to-Image Generation. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• S. Zhang, J. Cao, D. Cheng, X. Zhou, S. Wan, L. Gan, and D.-C. Zhan. Leveraging Conditional Dependence for Efficient World Model Denoising. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• Y.-C. Li, Z. Ling, T. Jiang, F. Zhang, P. Wang, L. Yuan, Z. Zhang, and Y. Yu. Multi-agent imitation by learning and sampling from factorized soft Q-Function. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• Y.-H. Yan, P. Zhao, and Z.-H. Zhou. Optimistic Online-to-Batch Conversions for Accelerated Convergence and Universality. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• L.-F. Li, Y.-Y. Qian, P. Zhao, and Z.-H. Zhou. Provably Efficient Online RLHF with One-Pass Reward Modeling. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• C. Lu, K. Xue, L. Yuan, Y. Wang, Y. Wang, S. Fu, and C. Qian. Sequential Multi-Agent Dynamic Algorithm Configuration. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• Y. Wang, G. Huzhang, Q.-G. Chen, Z. Xu, W. Luo, K. Zhang, and L. Zhang. SPACE: Noise Contrastive Estimation Stabilizes Self-Play Fine-Tuning for Large Language Models. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• Y. Wang, H.-L. Sun, G. Huzhang, Q.-G. Chen, Z. Xu, W. Luo, K. Zhang, and L. Zhang. Triplets Better Than Pairs: Towards Stable and Effective Self-Play Fine-Tuning for LLMs. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• F.-M. Luo, L. Yuan, and Y. Yu. Uncertainty-sensitive privileged learning. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• W.-B. Du, T. Qin, T.-Z. Wang, and Z.-H. Zhou. Variance-reduced long-term rehearsal learning with QP reformulation. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• B.-L. Wang, T. Wei, J.-X. Shi, Y.-F. Li, and M.-L. Zhang. X-Mahalanobis: Transformer Feature Mixing for Reliable OOD Detection. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• G. Liang, X. Liu, and J. Wu. GPLQ: A General, Practical, and Lightning QAT Method for Vision Transformers. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• Y.-K. Zhang, S. Lu, Q. Chen, W. Luo, D.-C. Zhan, and H.-J. Ye. Let the LLM Stick to Its Strengths: Learning to Route Economical LLM. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• S. Zhang, J. Cao, D. Cheng, X. Zhou, S. Wan, L. Gan, and D.-C. Zhan. Leveraging Conditional Dependence for Efficient World Model Denoising. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• J.-P. Jiang, Y. Xia, H.-L. Sun, S.Y. Lu, Q.-G. Chen, W.H. Luo, K.F. Zhang, D.-C.Zhan, and H.-J. Ye. Multimodal Tabular Reasoning with Privileged Structured Information. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

• J. Shao, and J. Wu. Who Reasons in the Large Language Models. In: Advances in Neural Information Processing Systems 38 (NeurIPS'25), 2025.

PRCV

• Z. Ding, M. Fu, and J. Wu. Dual DTL: Double-Side Compact Transfer Networks for Efficient ViT Adaptation. In: Proceedings of the 8th Chinese Conference on Pattern Recognition and Computer Vision (PRCV'25), 2025.

Top

Journal Article and Book

[A] [C] [E] [F] [G] [I] [J] [M] [N] [P] [S] [T]

ACM Transactions on Knowledge Discovery from Data

• K. M. Ting, Z. Zhuang, G. Pang, Z. Liu, T. Liang, and Q. Zhao. What Are Anomalies in a Network?. In: ACM Transactions on Knowledge Discovery from Data, 2025, 19(6):1-34.

Artificial Intelligence

• T.-Z. Wang, L. Tao, T. Qin, and Z.-H. Zhou. Estimating Possible Causal Effects with Latent Variables via Adjustment and Novel Rule Orientation. In: Artificial Intelligence, 2025, in press.

• H. Zhang, K. M. Ting, and Y. Zhu. Kernel-Bounded Clustering: Achieving the Objective of Spectral Clustering without Eigendecomposition. In: Artificial Intelligence, 2025, 350: 104440.

Communications Chemistry

• Y. Xu, Y. Ma, W. Xu, Z. Yang, and K. M. Ting. A large language model for deriving spectral embeddings for accurate compound identification in mass spectrometry. In: Communications Chemistry, 2025, 8(1): 326.

European Journal of Pharmaceutical Sciences

• L.-Y. H. Chen, T.-S. Liu, Z.-L. Zhang, F. Chen, D.-C. Zhan, Y. Yu, and G. Yu. Applying exposure-response analysis to enhance Mycophenolate Mofetil dosing precision in pediatric patients with immune-mediated renal diseases by machine learning models. In: European Journal of Pharmaceutical Sciences, 2025, 211: 107146.

Frontiers of Computer Science

• J.-Q. Yang, C. Dai, D. Ou, D. Li, J. Huang, D.-C. Zhan, X. Zeng, and Y. Yang. COURIER: Contrastive User Intention Reconstruction for Large-Scale Visual Recommendation. In: Frontiers of Computer Science, 2025, 19(7): 197602.

• R.-J. Wang, K. Xue, Y. Wang, P. Yang, H. Fu, Q. Fu, and C. Qian. Diversity from Human Feedback. In: Frontiers of Computer Science, 2025, in press.

• F. Xu, Z.-J. Zhou, J. Ni, and W. Gao. Interpretation with Baseline Shapley Value for Feature Groups on Tree Models. In: Frontiers of Computer Science, 2025, 19(5): 195316.

• Z. Zhu, H.-L. Tian, X. Chen, K. Zhang, and Y. Yu. Offline model-based reinforcement learning with causal structured world models. In: Frontiers of Computer Science, 2025, 19(4): 194347.

• C. Guan, K. Xue, C. Fan, F. Chen, L. Zhang, L. Yuan, C. Qian, and Y. Yu. Open and real-world human-AI coordination by heterogeneous training with communication. In: Frontiers of Computer Science, 2025, 19(4): 194314.

• Y. Shi, H.-J. Ye, D.-L. Man, X.-X. Han, D.-C. Zhan, Y. Jiang, and H.-J. Ye. Revisiting multi-dimensional classification from a dimension-wise perspective. In: Frontiers of Computer Science, 2025, 19(1): 191304.

• L.-Z. Guo, L.-H. Jia, J.-J. Shao, and Y.-F. Li. Robust Semi-supervised Learning in Open Environments. In: Frontiers of Computer Science, 2025, 19(8): 198345.

Genome Research

• H. Zhang, Y. Zhang, K. M. Ting, J. Zhang, and Q. Zhao. Kernel-bounded clustering for spatial transcriptomics enables scalable discovery of complex spatial domains. In: Genome Research, 2025, 35(2): 355-367.

IEEE Transactions on Knowledge and Data Engineering

• Y.-Y. Qian, Y. Bai, Z.-Y. Zhang, P. Zhao, and Z.-H. Zhou. Handling New Class in Online Label Shift. In: IEEE Transactions on Knowledge and Data Engineering, 2025, 37(9):5257-5270.

IEEE Transactions on Neural Networks and Learning Systems

• C. Guan, F. Chen, L. Yuan, Z. Zhang, and Y. Yu. Efficient Communication via Self-Supervised Information Aggregation for Online and Offline Multiagent Reinforcement Learning. In: IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(5): 9044-9056.

• F. Zhang, J. Li, Y.-C. Li, Z. Zhang, Y. Yu, and D. Ye. Improving Sample Efficiency of Reinforcement Learning with Background Knowledge from Large Language Models. In: IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(11): 19681-19692.

• H. Ding, C. Jia, Z. Zhang, C. Guan, F. Chen, L. Yuan, and Y. Yu. Learning to Coordinate With Different Teammates via Team Probing. In: IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(9): 15807-15821.

• L. Yuan, L. Li, Z. Zhang, F. Zhang, C. Guan, and Y. Yu. Multiagent Continual Coordination via Progressive Task Contextualization. In: IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(4): 6326-6340.

• J.-C. Pang, T. Xu, S. Jiang, Y.-R. Liu, and Y. Yu. Reinforcement Learning With Sparse-Executing Action via Sparsity Regularization. In: IEEE Transactions on Neural Networks and Learning Systems, 2025, 36(9): 16072-16084.

IEEE Transactions on Pattern Analysis and Machine Intelligence

• M. Fu, K. Zhu, Z. Ding, and J. Wu. DTL: Parameter- and Memory-Efficient Disentangled Vision Learning. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025, in press.

• Y.-C. Li, N. Chao, Z. Zhang, F. Zhang, L. Yuan, and Y. Yu. Generalizable Multi-Modal Adversarial Imitation Learning for Non-Stationary Dynamics. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025, 47(7): 5600-5612.

• Y. Shi, R.-X. Li, L. Gan, D.-C. Zhan, and H.-J. Ye. Generalized Conditional Similarity Learning via Semantic Matching. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025, 47(5): 3847-3862.

• D.-W. Zhou, Y. Zhang, Y. Wang, J. Ning, H.-J. Ye, D.-C. Zhan, and Z. Liu. Learning without forgetting for vision-language models. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025, 47(6): 4489-4504.

IEEE Transactions on Software Engineering

• Y.-L. Du, H. Sun, and M. Li. Post-Incorporating Code Structural Knowledge Into Pretrained Models via ICL for Code Translation. In: IEEE Transactions on Software Engineering, 2025, 51(11): 3038-3055.

Journal of Artificial Intelligence Research

• Z. Hang, K. Zhang, and K. M. Ting. Towards a Robust Persistence Diagram via Data-dependent Kernel. In: Journal of Artificial Intelligence Research, 2025, in press.

Journal of Computer Research and Development

• P. Tan, and Z.-H. Zhou. Tree-based Assembly for Learnwares from Heterogeneous Feature Spaces. In: Journal of Computer Research and Development, 2025, in press.

• J.-H. Wu, and Y. Jiang. Universal Approximation and Approximation Advantages of Quaternion-valued Neural Networks. In: Journal of Computer Research and Development, 2025, 62(5): 1205-1215.

Journal of Machine Learning Research

• L. Zhang, Y. Wang, G. Wang, J. Yi, and T. Yang. Universal Online Convex Optimization Meets Second-order Bounds. In: Journal of Machine Learning Research, 2025, 26(152): 1-53.

Machine Learning

• K. Zhang, H. Zhang, K. M. Ting, and T. Liang. A new filter for deformation-invariant persistence diagram. In: Machine Learning, 2025, 114(10): 233.

• Z.-H. Qi, D.-W. Zhou, Y.-R. Yao, H.-J. Ye, and D.-C. Zhan. Adaptive adapter routing for long-tailed class-incremental learning. In: Machine Learning, 2025, 114(3): 68.

• Y.-L. Du, Y. Li, Y.-F. Ma, and M. Li. Capturing the context-aware code change via dynamic control flow graph for commit message generation. In: Machine Learning, 2025, 114(4): 94.

• Y.-R. Liu, X.-H. Chen, S. Xiao, X. Yang, X. Qi, L. Zhou, Y. Yu, and F. Huang. Learning de-biased environment models for delivery incentive policy optimization on food delivery platforms. In: Machine Learning, 2025, 114(12): 262.

• H.-Y. He, Y. Liu, R.-B. Liu, Z. Xie, and M. Li. Probabilistic instance dependent label refinement for noisy label learning. In: Machine Learning, 2025, 114(5): 120.

Neural Networks

• C. Guan, T. Jiang, Y.-C. Li, Z. Zhang, L. Yuan, and Y. Yu. Constraining an Unconstrained Multi-agent Policy with offline data. In: Neural Networks, 2025, 186: 107253.

Pattern Recognition

• K. Zhu, Y.-Y. He, and J. Wu. Coarse Is Better? A New Pipeline Towards Self-Supervised Learning with Uncurated Images. In: Pattern Recognition, 2025, 161: 111324.

• X.-Y. Zhang, X.-K. Lu, Y.-L. Yin, H.-J. Ye, D.-C.Zhan, H.-J. Ye, and D.-C. Zhan. Efficient Sampling-based Gaussian Processes for few-shot semantic segmentation. In: Pattern Recognition, 2025, 164: 111542.

• H. Yu, Y. Du, and J. Wu. Reviving Undersampling for Long-tailed Learning. In: Pattern Recognition, 2025, 161: 111200.

Science China: Information Sciences

• M.-J. Yuan, X.-T. Bai, K.-R. Zhang, and W. Gao. On the encryption for graph foundation model inference of sparse graph. In: Science China: Information Sciences, 2025, 68: 160104.

• H.-L. Sun, D.-W. Zhou, D.-C. Zhan, and H.-J. Ye. Pilot: A pre-trained model-based continual learning toolbox. In: Science China: Information Sciences, 2025, 68(4):1-2.

Transactions on Machine Learning Research

• F. Chen, X. Chen, R.-J. Qin, C. Guan, L. Yuan, Z. Zhang, and Y. Yu. Efficient Multi-Agent Cooperation Learning through Teammate Lookahead. In: Transactions on Machine Learning Research, 2025, in press.

• J.-C. Pang, H.-B. Fan, P. Wang, J. Xiao, N. Tang, S.-H. Yang, C. Jia, M.-K. Xie, X. Chen, S.-J. Huang, and Y. Yu. Interactive Large Language Models for Reliable Answering under Incomplete Context. In: Transactions on Machine Learning Research, 2025, in press.

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