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 ]

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 ]

B. Peng, Y. Luo, Y. Zhang, Y. Li, Z. Fang.
ConjNorm: Tractable Density Estimation for Out-of-Distribution Detection.
In International Conference on Learning Representations (ICLR 2024), Published Online, 2024 .
[ Link ] [ CODE ]

Y. Lu, L. Chen, Y. Zhang, Y. Zhang, B. Han, Y.M. Cheung, H. Wang.
Federated Learning with Extremely Noisy Clients via Negative Distillation.
In Association for the Advancement of Artificial Intelligence (AAAI 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 ]

Z. Wang, Y. Zhang, Z. Fang, L. Lan, W. Yang, B. Han.
SODA: Robust Training of Test-Time Data Adaptors.
In Conference on Neural Information Processing Systems (NeurIPS 2023), Published Online, 2023 .
[ Link ] [ CODE ]

Q. Wang, Z. Fang, Y. Zhang, F. Liu, Y. Li, B. Han.
Learning to Augment Distributions for Out-of-distribution Detection.
In Conference on Neural Information Processing Systems (NeurIPS 2023), Published Online, 2023 .
[ Link ] [ CODE ]

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 ]

H. Li*, X. Wu, F. Lv, D. Liao, T.H. Li, Y. Zhang^†*, B. Han, M. Tan^†.
Hard Sample Matters a Lot in Zero-Shot Quantization.
In Conference on Computer Vision and Pattern Recognition (CVPR 2023), Published Online, 2023 .
[ Link ] [ CODE ]

Q. Wang, F. Liu, Y. Zhang, J. Zhang, C. Gong, T. Liu, B. Han.
Watermarking for Out-of-distribution Detection.
In Conference on Neural Information Processing Systems (NeurIPS 2022), Published Online, 2022 .
[ Link ] [ CODE ] [ Spotlight ]

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 ]

C. Wan, X. Shen, Y. Zhang, Z. Yin, X. Tian, F. Gao, J. Huang, X. Hua.
Meta Convolutional Neural Networks for Single Domain Generalization.
In Conference on Computer Vision and Pattern Recognition (CVPR 2022), Published Online, 2022 .
[ Link ]

Y. Lu, J. Liu, Y. Zhang, Y. Liu, X. Tian.
Prompt Distribution Learning.
In Conference on Computer Vision and Pattern Recognition (CVPR 2022), Published Online, 2022 .
[ Link ] [ CODE ]

Y. Chen, H. Yang, Y. Zhang, K. Ma, T. Liu, B. Han, J. Cheng.
Understanding and Improving Graph Injection Attack by Promoting Unnoticeability.
In International Conference on Learning Representations (ICLR 2022), Published Online, 2022 .
[ Link ] [ 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 ]

K. Yang, T. Zhou, Y. Zhang, X. Tian, D. Tao.
Class-Disentanglement and Applications in Adversarial Detection and Defense.
In Conference on Neural Information Processing Systems (NeurIPS 2021), Published Online, 2021 .
[ 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 ]

Y. Zhang, X. Tian, Y. Li, X. Wang, D. Tao.
Principal Component Adversarial Example.
IEEE Transactions on Image Processing, Accepted, 2020 (ERA&CORE A*).
[ Link ]