| Efficient private ERM for smooth objectives J Zhang, K Zheng, W Mou, L Wang arXiv preprint arXiv:1703.09947, 2017 | 96 | 2017 |
| Generalization bounds of sgld for non-convex learning: Two theoretical viewpoints W Mou, L Wang, X Zhai, K Zheng Conference on Learning Theory, 605-638, 2018 | 83 | 2018 |
| Collect at once, use effectively: Making non-interactive locally private learning possible K Zheng, W Mou, L Wang International Conference on Machine Learning, 4130-4139, 2017 | 32 | 2017 |
| Efficient online portfolio with logarithmic regret H Luo, CY Wei, K Zheng Advances in Neural Information Processing Systems 31, 2018 | 28 | 2018 |
| Locally differentially private (contextual) bandits learning K Zheng, T Cai, W Huang, Z Li, L Wang Advances in Neural Information Processing Systems 33, 12300-12310, 2020 | 16 | 2020 |
| Equipping experts/bandits with long-term memory K Zheng, H Luo, I Diakonikolas, L Wang Advances in Neural Information Processing Systems 32, 2019 | 10 | 2019 |
| Combinatorial semi-bandit in the non-stationary environment W Chen, L Wang, H Zhao, K Zheng Uncertainty in Artificial Intelligence, 865-875, 2021 | 5 | 2021 |
| (Locally) Differentially Private Combinatorial Semi-Bandits X Chen, K Zheng, Z Zhou, Y Yang, W Chen, L Wang International Conference on Machine Learning, 1757-1767, 2020 | 1 | 2020 |
| Combinatorial Semi-Bandit in the Non-Stationary Environment Supplementary Material W Chen, L Wang, H Zhao, K Zheng | | |
