| Adversarial example does good: Preventing painting imitation from diffusion models via adversarial examples C Liang, X Wu, Y Hua, J Zhang, Y Xue, T Song, Z Xue, R Ma, H Guan ICML 2023, 2023 | 96 | 2023 |
| Improving Bayesian Neural Networks by Adversarial Sampling J Zhang, Y Hua, T Song, H Wang, Z Xue, R Ma, H Guan Proceedings of the AAAI Conference on Artificial Intelligence 36 (9), 10110 …, 2022 | 13 | 2022 |
| Robust Bayesian Neural Networks by Spectral Expectation Bound Regularization J Zhang, Y Hua, Z Xue, T Song, C Zheng, R Ma, H Guan Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 10 | 2021 |
| Hierarchical Satellite System Graph for Approximate Nearest Neighbor Search on Big Data J Zhang, R Ma, T Song, Y Hua, Z Xue, C Guan, H Guan ACM/IMS Transactions on Data Science (TDS) 2 (4), 1-15, 2022 | 4 | 2022 |
| CGI-DM: Digital Copyright Authentication for Diffusion Models via Contrasting Gradient Inversion X Wu, Y Hua, C Liang, J Zhang, H Wang, T Song, H Guan Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 3 | 2024 |
| Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior (Extended Version) P Ma, R Ding, Q Fu, J Zhang, S Wang, S Han, D Zhang | 2 | 2024 |
| Revealing the Unseen: Guiding Personalized Diffusion Models to Expose Training Data X Wu, J Zhang, S Wu arXiv preprint arXiv:2410.03039, 2024 | | 2024 |
| Scalable Differentiable Causal Discovery in the Presence of Latent Confounders with Skeleton Posterior P Ma, R Ding, Q Fu, J Zhang, S Wang, S Han, D Zhang Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and …, 2024 | | 2024 |
| Exploring Diffusion Models' Corruption Stage in Few-Shot Fine-tuning and Mitigating with Bayesian Neural Networks X Wu, J Zhang, Y Hua, B Lyu, H Wang, T Song, H Guan arXiv preprint arXiv:2405.19931, 2024 | | 2024 |
| Information Bound and Its Applications in Bayesian Neural Networks J Zhang, Y Hua, T Song, H Wang, Z Xue, R Ma, H Guan European Conference on Artificial Intelligence, 2023 | | 2023 |
| Learning Identifiable Causal Structures with Pairwise Representation J Zhang, R Ding, Q Fu, Y Hua, H Guan, S Han, D Zhang | | |
