| Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network X Liu, Y Li, W Chongruo, CJ Hsieh Advances in International Conference on Learning Representations, 2019 | 198 | 2019 |
| A review of adversarial attack and defense for classification methods Y Li, M Cheng, CJ Hsieh, TCM Lee The American Statistician 76 (4), 329-345, 2022 | 47 | 2022 |
| Towards Robustness of Deep Neural Networks via Regularization Y Li, MR Min, T Lee, W Yu, E Kruus, CJ Hsieh International Conference on Computer Vision, 2021 | 24* | 2021 |
| Learning from Group Comparisons: Exploiting Higher Order Interactions Y Li, M Cheng, K Fujii, F Hsieh, CJ Hsieh Advances in Neural Information Processing Systems, 4986-4995, 2018 | 23 | 2018 |
| Scalable demand-aware recommendation J Yi, CJ Hsieh, K Varshney, L Zhang, Y Li Advances in Neural Information Processing Systems, 2017 | 23 | 2017 |
| l-Arginine supplementation in severe asthma SY Liao, MR Showalter, AL Linderholm, L Franzi, C Kivler, Y Li, MR Sa, ... JCI insight 5 (13), 2020 | 21 | 2020 |
| Detecting adversarial examples with bayesian neural network Y Li, T Tang, CJ Hsieh, T Lee arXiv preprint arXiv:2105.08620, 2021 | 7 | 2021 |
| Uncertainty quantification for high-dimensional sparse nonparametric additive models Q Gao, RCS Lai, TCM Lee, Y Li Technometrics 62 (4), 513-524, 2020 | 7 | 2020 |
| ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation KWC Fan Yin, Yao Li, Cho-Jui Hsieh Conference on Empirical Methods in Natural Language Processing, 6567–6584, 2022 | 5* | 2022 |
Cho-Jui HsiehUniversity of California, Los AngelesVerified email at cs.ucla.edu
