| More adaptive algorithms for adversarial bandits CY Wei, H Luo Conference On Learning Theory, 1263-1291, 2018 | 184 | 2018 |
| Online reinforcement learning in stochastic games CY Wei, YT Hong, CJ Lu Advances in Neural Information Processing Systems 30, 2017 | 149 | 2017 |
| A new algorithm for non-stationary contextual bandits: Efficient, optimal and parameter-free Y Chen, CW Lee, H Luo, CY Wei Conference on Learning Theory, 696-726, 2019 | 141 | 2019 |
| Linear last-iterate convergence in constrained saddle-point optimization CY Wei, CW Lee, M Zhang, H Luo International Conference on Learning Representations, 2021 | 139* | 2021 |
| Efficient contextual bandits in non-stationary worlds H Luo, CY Wei, A Agarwal, J Langford Conference On Learning Theory, 1739-1776, 2018 | 138 | 2018 |
| Non-stationary reinforcement learning without prior knowledge: An optimal black-box approach CY Wei, H Luo Conference on learning theory, 4300-4354, 2021 | 117 | 2021 |
| Model-free reinforcement learning in infinite-horizon average-reward markov decision processes CY Wei, MJ Jahromi, H Luo, H Sharma, R Jain International conference on machine learning, 10170-10180, 2020 | 113 | 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 | 112 | 2021 |
| Beating stochastic and adversarial semi-bandits optimally and simultaneously J Zimmert, H Luo, CY Wei International Conference on Machine Learning, 7683-7692, 2019 | 95 | 2019 |
| Independent policy gradient for large-scale markov potential games: Sharper rates, function approximation, and game-agnostic convergence D Ding, CY Wei, K Zhang, M Jovanovic International Conference on Machine Learning, 5166-5220, 2022 | 85 | 2022 |
| Tracking the best expert in non-stationary stochastic environments CY Wei, YT Hong, CJ Lu Advances in neural information processing systems 29, 2016 | 70 | 2016 |
| 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 | 66 | 2020 |
| Learning infinite-horizon average-reward mdps with linear function approximation CY Wei, MJ Jahromi, H Luo, R Jain International Conference on Artificial Intelligence and Statistics, 3007-3015, 2021 | 65 | 2021 |
| A model selection approach for corruption robust reinforcement learning CY Wei, C Dann, J Zimmert International Conference on Algorithmic Learning Theory, 1043-1096, 2022 | 61 | 2022 |
| Efficient online portfolio with logarithmic regret H Luo, CY Wei, K Zheng Advances in neural information processing systems 31, 2018 | 60 | 2018 |
| Policy optimization in adversarial mdps: Improved exploration via dilated bonuses H Luo, CY Wei, CW Lee Advances in Neural Information Processing Systems 34, 22931-22942, 2021 | 55 | 2021 |
| 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 | 53 | 2021 |
| Improved path-length regret bounds for bandits S Bubeck, Y Li, H Luo, CY Wei Conference On Learning Theory, 508-528, 2019 | 52 | 2019 |
| Impossible tuning made possible: A new expert algorithm and its applications L Chen, H Luo, CY Wei Conference on Learning Theory, 1216-1259, 2021 | 48 | 2021 |
| Personalization improves privacy-accuracy tradeoffs in federated learning A Bietti, CY Wei, M Dudik, J Langford, S Wu International Conference on Machine Learning, 1945-1962, 2022 | 46 | 2022 |
Haipeng LuoAssociate Professor, University of Southern CaliforniaVerified email at usc.edu
