| How does mixup help with robustness and generalization? L Zhang, Z Deng, K Kawaguchi, A Ghorbani, J Zou arXiv preprint arXiv:2010.04819, 2020 | 234 | 2020 |
| Analyzing and mitigating object hallucination in large vision-language models Y Zhou, C Cui, J Yoon, L Zhang, Z Deng, C Finn, M Bansal, H Yao arXiv preprint arXiv:2310.00754, 2023 | 77 | 2023 |
| An unconstrained layer-peeled perspective on neural collapse W Ji, Y Lu, Y Zhang, Z Deng, WJ Su arXiv preprint arXiv:2110.02796, 2021 | 63 | 2021 |
| When and how mixup improves calibration L Zhang, Z Deng, K Kawaguchi, J Zou International Conference on Machine Learning, 26135-26160, 2022 | 56 | 2022 |
| Adversarial training helps transfer learning via better representations Z Deng, L Zhang, K Vodrahalli, K Kawaguchi, JY Zou Advances in Neural Information Processing Systems 34, 25179-25191, 2021 | 45 | 2021 |
| Toward better generalization bounds with locally elastic stability Z Deng, H He, W Su International Conference on Machine Learning, 2590-2600, 2021 | 35 | 2021 |
| The power of contrast for feature learning: A theoretical analysis W Ji, Z Deng, R Nakada, J Zou, L Zhang Journal of Machine Learning Research 24 (330), 1-78, 2023 | 31 | 2023 |
| Improving adversarial robustness via unlabeled out-of-domain data Z Deng, L Zhang, A Ghorbani, J Zou International Conference on Artificial Intelligence and Statistics, 2845-2853, 2021 | 25 | 2021 |
| How does information bottleneck help deep learning? K Kawaguchi, Z Deng, X Ji, J Huang International Conference on Machine Learning, 16049-16096, 2023 | 23 | 2023 |
| Decision-aware conditional gans for time series data H Sun, Z Deng, H Chen, D Parkes Proceedings of the Fourth ACM International Conference on AI in Finance, 36-45, 2023 | 21 | 2023 |
| Robustness implies generalization via data-dependent generalization bounds K Kawaguchi, Z Deng, K Luh, J Huang International conference on machine learning, 10866-10894, 2022 | 21 | 2022 |
| Representation via representations: Domain generalization via adversarially learned invariant representations Z Deng, F Ding, C Dwork, R Hong, G Parmigiani, P Patil, P Sur arXiv preprint arXiv:2006.11478, 2020 | 20 | 2020 |
| Understanding multimodal contrastive learning and incorporating unpaired data R Nakada, HI Gulluk, Z Deng, W Ji, J Zou, L Zhang International Conference on Artificial Intelligence and Statistics, 4348-4380, 2023 | 19 | 2023 |
| Happymap: A generalized multi-calibration method Z Deng, C Dwork, L Zhang arXiv preprint arXiv:2303.04379, 2023 | 17 | 2023 |
| Interpreting robust optimization via adversarial influence functions Z Deng, C Dwork, J Wang, L Zhang International Conference on Machine Learning, 2464-2473, 2020 | 16 | 2020 |
| Fifa: Making fairness more generalizable in classifiers trained on imbalanced data Z Deng, J Zhang, L Zhang, T Ye, Y Coley, WJ Su, J Zou arXiv preprint arXiv:2206.02792, 2022 | 15 | 2022 |
| Learning and forgetting unsafe examples in large language models J Zhao, Z Deng, D Madras, J Zou, M Ren arXiv preprint arXiv:2312.12736, 2023 | 13 | 2023 |
| Scaffolding sets M Burhanpurkar, Z Deng, C Dwork, L Zhang arXiv preprint arXiv:2111.03135, 2021 | 13 | 2021 |
| Reinforcement learning with stepwise fairness constraints Z Deng, H Sun, ZS Wu, L Zhang, DC Parkes arXiv preprint arXiv:2211.03994, 2022 | 12 | 2022 |
| Towards understanding the dynamics of the first-order adversaries Z Deng, H He, J Huang, W Su International Conference on Machine Learning, 2484-2493, 2020 | 11 | 2020 |
Linjun ZhangAssistant Professor of Statistics, Rutgers University在 rutgers.edu 的电子邮件经过验证
