| Linear last-iterate convergence in constrained saddle-point optimization CY Wei, CW Lee, M Zhang, H Luo arXiv preprint arXiv:2006.09517, 2020 | 96 | 2020 |
| Last-iterate convergence of decentralized optimistic gradient descent/ascent in infinite-horizon competitive Markov games CY Wei, CW Lee, M Zhang, H Luo Conference on learning theory, 4259-4299, 2021 | 91 | 2021 |
| Bias no more: high-probability data-dependent regret bounds for adversarial bandits and mdps CW Lee, H Luo, CY Wei, M Zhang Advances in neural information processing systems 33, 15522-15533, 2020 | 50 | 2020 |
| Achieving near instance-optimality and minimax-optimality in stochastic and adversarial linear bandits simultaneously CW Lee, H Luo, CY Wei, M Zhang, X Zhang International Conference on Machine Learning, 6142-6151, 2021 | 44 | 2021 |
| No-regret learning in time-varying zero-sum games M Zhang, P Zhao, H Luo, ZH Zhou International Conference on Machine Learning, 26772-26808, 2022 | 31 | 2022 |
| A closer look at small-loss bounds for bandits with graph feedback CW Lee, H Luo, M Zhang Conference on Learning Theory, 2516-2564, 2020 | 21 | 2020 |
| Random mask: Towards robust convolutional neural networks T Luo, T Cai, M Zhang, S Chen, L Wang arXiv preprint arXiv:2007.14249, 2020 | 15 | 2020 |
| Linear last-iterate convergence for matrix games and stochastic games CW Lee, H Luo, CY Wei, M Zhang arXiv e-prints, arXiv: 2006.09517, 2020 | 15 | 2020 |
| Autobidders with budget and roi constraints: Efficiency, regret, and pacing dynamics B Lucier, S Pattathil, A Slivkins, M Zhang arXiv preprint arXiv:2301.13306, 2023 | 13 | 2023 |
| Corralling a larger band of bandits: A case study on switching regret for linear bandits H Luo, M Zhang, P Zhao, ZH Zhou Conference on Learning Theory, 3635-3684, 2022 | 11 | 2022 |
| No-regret learning in two-echelon supply chain with unknown demand distribution M Zhang, S Chen, H Luo, Y Wang International Conference on Artificial Intelligence and Statistics, 3270-3298, 2023 | 6 | 2023 |
| Improved high-probability regret for adversarial bandits with time-varying feedback graphs H Luo, H Tong, M Zhang, Y Zhang International Conference on Algorithmic Learning Theory, 1074-1100, 2023 | 6 | 2023 |
| Adaptive bandit convex optimization with heterogeneous curvature H Luo, M Zhang, P Zhao Conference on Learning Theory, 1576-1612, 2022 | 4 | 2022 |
| Advancing query rewriting in e-commerce via shopping intent learning M Zhang, Y Wu, R Rustamov, H Zhu, H Shi, Y Wu, L Tang, Z Zhang, ... | 3 | 2022 |
| Online learning in contextual second-price pay-per-click auctions M Zhang, H Luo International Conference on Artificial Intelligence and Statistics, 2395-2403, 2024 | 2 | 2024 |
| Practical contextual bandits with feedback graphs M Zhang, Y Zhang, O Vrousgou, H Luo, P Mineiro Advances in Neural Information Processing Systems 36, 2024 | 2 | 2024 |
| Defective Convolutional Networks T Luo, T Cai, M Zhang, S Chen, D He, L Wang arXiv preprint arXiv:1911.08432, 2019 | 2 | 2019 |
| Contextual Multinomial Logit Bandits with General Value Functions M Zhang, H Luo arXiv preprint arXiv:2402.08126, 2024 | | 2024 |
| Efficient Contextual Bandits with Uninformed Feedback Graphs M Zhang, Y Zhang, H Luo, P Mineiro arXiv preprint arXiv:2402.08127, 2024 | | 2024 |
| Supply Chain Coordination with Unknown Demand Distribution: No-Regret Learning S Chen, H Luo, Y Wang, M Zhang Available at SSRN 4456201, 2023 | | 2023 |
