J. Gao, H. Yu, J. Cao, R. Bu, J. Tang, N. Zhu, Y. Zhang.
Generating then Refining for Reliable Knowledge Base Question Answering.
In Annual Meeting of the Association for Computational Linguistics (ACL 2026) , Published Online, 2026.
[ Link ]

Z. Yang, Y. Zhang^†, W. Xue, D. Fang, B. Han, Y. Guo^†.
Conditional Equivalence of DPO and RLHF: Implicit Assumption, Failure Modes, and Provable Alignment.
In International Conference on Machine Learning (ICML 2026) (Spotlight), Published Online, 2026.
[ Link ] [ CODE ] [ Spotlight ]

Z. Yang*, Z. Zhang*, X. Jia*, J. Song*, W. Xue*, Y. Zhang^†, Y. Guo^†.
ClawNet: Human-Symbiotic Agent Network for Cross-User Autonomous Cooperation.
In ICML 2026 Workshop on Technical AI Governance Research (TAIGR), Published Online, 2026.
[ Link ] [ CODE ]

Y. Zhang, J. Gao, J. Lu.
Enhancing LLMs for Knowledge Base Question Answering by Chain-of-Decomposition.
In International Conference on Learning Representations (ICLR 2026) , Published Online, 2026.
[ Link ] [ CODE ] [ ]

C. Jiang, Y. Zhang, Y. Cai, C.M. Chan, Y. Liu, M. Chen, W. Xue, Y. Guo.
Semantic Voting: A Self-Evaluation-Free Approach for Efficient LLM Self-Improvement on Unverifiable Open-ended Tasks.
In International Conference on Learning Representations (ICLR 2026) , Published Online, 2026.
[ Link ] [ CODE ] [ ]

Y. Zhang, J. Nie, X. Tian, M. Gong, K. Zhang, B. Han.
Detecting Generated Images by Fitting Natural Image Distributions.
In Conference on Neural Information Processing Systems (NeurIPS 2025) (Spotlight), Published Online, 2025.
[ Link ] [ CODE ] [ Spotlight ]

J. Nie, Y. Zhang, T. Liu, Y.M. Cheung, B. Han, X. Tian.
Epistemic Uncertainty for Generated Image Detection.
In Conference on Neural Information Processing Systems (NeurIPS 2025), Published Online, 2025.
[ Link ] [ CODE ]

Z. Yang, Y. Zhang, C. Li, Y.M. Cheung, B. Han, Y. Yuan.
FedGPS: Statistical Rectification Against Data Heterogeneity in Federated Learning.
In Conference on Neural Information Processing Systems (NeurIPS 2025), Published Online, 2025.
[ Link ] [ CODE ]

Y. Zhang, J. Lu, B. Peng, Z. Fang, Y.M. Cheung.
Learning to Shape In-distribution Feature Space for Out-of-distribution Detection.
In Conference on Neural Information Processing Systems (NeurIPS 2024), Published Online, 2024.
[ Link ] [ CODE ]

Z. Tang, Y. Zhang, P. Dong, Y.M. Cheung, A.C. Zhou, B. Han, X. Chu.
FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion.
In Conference on Neural Information Processing Systems (NeurIPS 2024) (Spotlight), Published Online, 2024.
[ Link ] [ CODE ] [ Spotlight ]

W. Zhang, C. Wan, Y. Zhang^†, Y.M. Cheung, X. Tian, X. Shen^†, J. Ye.
Interpreting and Improving Large Language Models in Arithmetic Calculation.
In International Conference on Machine Learning (ICML 2024) (Oral), Published Online, 2024.
[ Link ] [ CODE ] [ Oral ]

Y. Zhang, Z. Yang, X. Tian, N. Wang, T. Liu, B. Han.
Robust Training of Federated Models with Extremely Label Deficiency.
In International Conference on Learning Representations (ICLR 2024), Published Online, 2024.
[ Link ] [ CODE ]

P. Zheng*, Y. Zhang*, Z. Fang, T. Liu, D. Lian, B. Han.
Beyond Linear Spherical Interpolation: Noise Correction for Image Interpolation with Diffusion Models.
In International Conference on Learning Representations (ICLR 2024) (Spotlight), Published Online, 2024.
[ Link ] [ CODE ] [ Spotlight ]

J. Nie, Y. Zhang^†, Z. Fang, T. Liu, B. Han, X. Tian^†.
Out-of-Distribution Detection with Negative Prompts.
In International Conference on Learning Representations (ICLR 2024), Published Online, 2024.
[ Link ] [ CODE ]

R. Dai, Y. Zhang, A. Li, T. Liu, X. Yang, B. Han.
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting.
In International Conference on Learning Representations (ICLR 2024), Published Online, 2024.
[ Link ] [ CODE ]

Z. Tang, Y. Zhang, S.Shi, X. Tian, T. Liu, B. Han, X. Chu.
FedImpro: Measuring and Improving Client Update in Federated Learning.
In International Conference on Learning Representations (ICLR 2024), Published Online, 2024.
[ Link ] [ CODE ]

Z. Yang*, Y. Zhang*, Y. Zheng, X. Tian, H. Peng, T. Liu, B. Han.
FedFed: Feature Distillation against Data Heterogeneity in Federated.
In Conference on Neural Information Processing Systems (NeurIPS 2023), Published Online, 2023.
[ Link ] [ CODE ]

M. Yang, Z. Fang, Y. Zhang^†, Y. Du, F. Liu, J.F Ton, J. Wang^†.
Invariant Learning via Probability of Sufficient and Necessary Causes.
In Conference on Neural Information Processing Systems (NeurIPS 2023) (Spotlight), Published Online, 2023.
[ Link ] [ CODE ] [ Spotlight ]

R. Dai, Y. Zhang^†, Z. Fang, B. Han, X. Tian^†.
Moderately Distributional Exploration for Domain Generalization.
In International Conference on Machine Learning (ICML 2023), Published Online, 2023.
[ Link ] [ CODE ]

C. Sun, Y. Zhang, W. Chaoqun, Q. Wang, Y. Li, T. Liu, B. Han, X. Tian.
Towards Lightweight Black-Box Attacks against Deep Neural Networks.
In Conference on Neural Information Processing Systems (NeurIPS 2022), Published Online, 2022.
[ Link ] [ CODE ]

Y. Chen, Y. Zhang, H. Yang, K. Ma, B. Xie, T. Liu, B. Han, J. Cheng.
Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs.
In Conference on Neural Information Processing Systems (NeurIPS 2022), Published Online, 2022.
[ Link ] [ CODE ] [ Spotlight ]

Z. Tang*, Y. Zhang*, S. Shi, X. He, B. Han, X. Chu.
Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning
In International Conference on Machine Learning (ICML 2022), Published Online, 2022.
[ arXiv ] [ CODE ]

Y. Zhang, M. Gong, T. Liu, G. Niu, X. Tian, B. Han, B. Schölkopf, K. Zhang.
CausalAdv: Adversarial Robustness Through the Lens of Causality.
In International Conference on Learning Representations (ICLR 2022), Published Online, 2022.
[ Link ] [ CODE ]

Y. Zhang, Y. Li, T. Liu, X. Tian.
Dual-Path Distillation: A Unified Framework to Improve Black-Box Attacks.
In International Conference on Machine Learning (ICML 2020), Published Online, 2020.
[ Link ]

X. Yang, R. Dai, Y. Zhang^†, A. Li, T. Liu, B. Han^†.
Co-Boosting++: Coupled Optimization of Data and Ensemble for One-Shot Federated Learning.
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2026.
[ Link ]

Y. Peng, Y. Zhang, Y.M. Cheung^†.
Semantic-Guided Fine-Tuning of Foundation Model for Long-Tailed Visual Recognition.
International Journal of Computer Vision (IJCV), 2026.
[ Link ]

R. Zeng, Z. Yang, R. Yu, Y. Zhang^†.
Supplementary Prompt Learning for Vision-Language Models.
International Journal of Computer Vision (IJCV), 2025.
[ Link ]

J. Gao, H. Yu, Y.M. Cheung, R. Wong, Y. Zhang^†.
Shaping Pre-Trained Language Models for Task-Specific Embedding Generation Via Consistency Calibration.
Neural Networks (NN), 2025.
[ Link ]

Y. Zhang, X. Tian
Consistent Prompt Learning for Vision-language Models.
Knowledge-Based Systems, 2025.
[ Link ]

J. Nie, Y. Luo, S. Ye, Y. Zhang^†, X. Tian^†, Z. Fang.
Out-of-Distribution Detection with Virtual Outlier Smoothing.
International Journal of Computer Vision (IJCV), 2024.
[ Link ]

Y. Zhang, X. Tian, Y. Li, X. Wang, D. Tao.
Principal Component Adversarial Example.
IEEE Transactions on Image Processing (TIP), 2020.
[ Link ]