| Backpropagating Linearly Improves Transferability of Adversarial Examples Y Guo, Q Li, H Chen NeurIPS 2020, 2020 | 94 | 2020 |
| Practical No-box Adversarial Attacks against DNNs Q Li, Y Guo, H Chen NeurIPS 2020, 2020 | 55 | 2020 |
| Yet Another Intermediate-Level Attack Q Li, Y Guo, H Chen ECCV 2020, 2020 | 41 | 2020 |
| Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial Examples Q Li, Y Guo, W Zuo, H Chen ICLR 2023, 2023 | 20 | 2023 |
| Adversarial Contrastive Learning via Asymmetric InfoNCE Q Yu, J Lou, X Zhan, Q Li, W Zuo, Y Liu, J Liu ECCV 2022, 2022 | 15 | 2022 |
| Squeeze Training for Adversarial Robustness Q Li, Y Guo, W Zuo, H Chen ICLR 2023, 2023 | 12* | 2023 |
| An Intermediate-level Attack Framework on The Basis of Linear Regression Y Guo, Q Li, W Zuo, H Chen TPAMI 2022, 2022 | 9 | 2022 |
| Improving Adversarial Transferability via Intermediate-level Perturbation Decay Q Li, Y Guo, W Zuo, H Chen NeurIPS 2023, 2023 | 2 | 2023 |
| Towards Evaluating Transfer-based Attacks Systematically, Practically, and Fairly Q Li, Y Guo, W Zuo, H Chen NeurIPS 2023, 2024 | 1 | 2024 |
| Improving transferability of adversarial examples via bayesian attacks Q Li, Y Guo, X Yang, W Zuo, H Chen arXiv preprint arXiv:2307.11334, 2023 | 1 | 2023 |
| DualAug: Exploiting Additional Heavy Augmentation with OOD Data Rejection Z Wang, Y Guo, Q Li, G Yang, W Zuo arXiv preprint arXiv:2310.08139, 2023 | | 2023 |
| On Steering Multi-Annotations per Sample for Multi-Task Learning Y Li, Y Guo, Q Li, H Zhang, W Zuo arXiv preprint arXiv:2203.02946, 2022 | | 2022 |
Hao ChenProfessor of Computer Science, UC Davis. IEEE FellowVerified email at ucdavis.edu
