| 2026 | KDD | Exploring Diffusion Models' Corruption Stage in Few-Shot Fine-tuning and Mitigating with Bayesian Neural Networks. | Xiaoyu Wu, Jiaru Zhang, Yang Hua, Bohan Lyu, Hao Wang, Tao Song, Haibing Guan |
| 2025 | AISTATS | Learning Identifiable Structures Helps Avoid Bias in DNN-based Supervised Causal Learning. | Jiaru Zhang, Rui Ding, Qiang Fu, Bojun Huang, Zizhen Deng, Yang Hua, Haibing Guan, Shi Han, Dongmei Zhang |
| 2025 | ICCV | Stealthy Backdoor Attack in Federated Learning via Adaptive Layer-Wise Gradient Alignment. | Qingqian Yang, Peishen Yan, Xiaoyu Wu, Jiaru Zhang, Tao Song, Yang Hua, Hao Wang, Liangliang Wang, Haibing Guan |
| 2025 | ICML | Leveraging Model Guidance to Extract Training Data from Personalized Diffusion Models. | Xiaoyu Wu, Jiaru Zhang, Steven Wu |
| 2024 | CVPR | CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion. | Xiaoyu Wu, Yang Hua, Chumeng Liang, Jiaru Zhang, Hao Wang, Tao Song, Haibing Guan |
| 2024 | KDD | Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior. | Pingchuan Ma, Rui Ding, Qiang Fu, Jiaru Zhang, Shuai Wang, Shi Han, Dongmei Zhang |
| 2023 | ECAI | Information Bound and Its Applications in Bayesian Neural Networks. | Jiaru Zhang, Yang Hua, Tao Song, Hao Wang, Zhengui Xue, Ruhui Ma, Haibing Guan |
| 2023 | ICML | Adversarial Example Does Good: Preventing Painting Imitation from Diffusion Models via Adversarial Examples. | Chumeng Liang, Xiaoyu Wu, Yang Hua, Jiaru Zhang, Yiming Xue, Tao Song, Zhengui Xue, Ruhui Ma, Haibing Guan |
| 2022 | AAAI | Improving Bayesian Neural Networks by Adversarial Sampling. | Jiaru Zhang, Yang Hua, Tao Song, Hao Wang, Zhengui Xue, Ruhui Ma, Haibing Guan |
| 2021 | CVPR | Robust Bayesian Neural Networks by Spectral Expectation Bound Regularization. | Jiaru Zhang, Yang Hua, Zhengui Xue, Tao Song, Chengyu Zheng, Ruhui Ma, Haibing Guan |