| Efficient algorithms for generalized linear bandits with heavy-tailed rewards B Xue, Y Wang, Y Wan, J Yi, L Zhang Advances in Neural Information Processing Systems 36, 2024 | 2 | 2024 |
| Fedavg converges to zero training loss linearly for overparameterized multi-layer neural networks B Song, P Khanduri, X Zhang, J Yi, M Hong International Conference on Machine Learning, 32304-32330, 2023 | 3 | 2023 |
| Stochastic graphical bandits with heavy-tailed rewards Y Gou, J Yi, L Zhang Uncertainty in Artificial Intelligence, 734-744, 2023 | 2 | 2023 |
| Training meta-surrogate model for transferable adversarial attack Y Qin, Y Xiong, J Yi, CJ Hsieh Proceedings of the AAAI Conference on Artificial Intelligence 37 (8), 9516-9524, 2023 | 15 | 2023 |
| Federated User Modeling from Hierarchical Information Q Liu, J Wu, Z Huang, H Wang, Y Ning, M Chen, E Chen, J Yi, B Zhou ACM Transactions on Information Systems 41 (2), 1-33, 2023 | 4 | 2023 |
| Trustworthy AI: From principles to practices B Li, P Qi, B Liu, S Di, J Liu, J Pei, J Yi, B Zhou ACM Computing Surveys 55 (9), 1-46, 2023 | 287 | 2023 |
| FedAvg Converges to Zero Training Loss Linearly: The Power of Overparameterized Multi-Layer Neural Networks B Song, P Khanduri, X Zhang, J Yi, M Hong | 2 | 2023 |
| Robust text captchas using adversarial examples R Shao, Z Shi, J Yi, PY Chen, CJ Hsieh 2022 IEEE International Conference on Big Data (Big Data), 1495-1504, 2022 | 18 | 2022 |
| Smoothed online convex optimization based on discounted-normal-predictor L Zhang, W Jiang, J Yi, T Yang Advances in Neural Information Processing Systems 35, 4928-4942, 2022 | 4 | 2022 |
| Can adversarial training be manipulated by non-robust features? L Tao, L Feng, H Wei, J Yi, SJ Huang, S Chen Advances in Neural Information Processing Systems 35, 26504-26518, 2022 | 10 | 2022 |
| With False Friends Like These, Who Can Notice Mistakes? L Tao, L Feng, J Yi, S Chen Proceedings of the AAAI Conference on Artificial Intelligence 36 (8), 8458-8466, 2022 | 3 | 2022 |
| A simple yet universal strategy for online convex optimization L Zhang, G Wang, J Yi, T Yang International Conference on Machine Learning, 26605-26623, 2022 | 15 | 2022 |
| Time-series representation learning via random time warping MJ Witbrock, L Wu, C Xiao, J Yi US Patent 11,366,990, 2022 | 8 | 2022 |
| Adaptive feature generation for online continual learning from imbalanced data Y Jian, J Yi, L Zhang Pacific-Asia Conference on Knowledge Discovery and Data Mining, 276-289, 2022 | 4 | 2022 |
| Method and system for time series representation learning via dynamic time warping Q Lei, W Sun, R Vaculin, J Yi US Patent 11,301,773, 2022 | 4 | 2022 |
| How to robustify black-box ml models? a zeroth-order optimization perspective Y Zhang, Y Yao, J Jia, J Yi, M Hong, S Chang, S Liu arXiv preprint arXiv:2203.14195, 2022 | 20 | 2022 |
| Method and system for time series representation learning via dynamic time warping Q Lei, W Sun, R Vaculin, J Yi US Patent 11,281,994, 2022 | 7 | 2022 |
| Adversarial machine learning for good PY Chen AAAI Conference on Artificial Intelligence, 2022 | 2 | 2022 |
| Understanding clipping for federated learning: Convergence and client-level differential privacy X Zhang, X Chen, M Hong, ZS Wu, J Yi International Conference on Machine Learning, ICML 2022, 2022 | 73 | 2022 |
| On the convergence and robustness of adversarial training Y Wang, X Ma, J Bailey, J Yi, B Zhou, Q Gu arXiv preprint arXiv:2112.08304, 2021 | 396 | 2021 |
Cho-Jui HsiehUniversity of California, Los Angeles在 cs.ucla.edu 的电子邮件经过验证
