| Wizardmath: Empowering mathematical reasoning for large language models via reinforced evol-instruct H Luo, Q Sun, C Xu, P Zhao, J Lou, C Tao, X Geng, Q Lin, S Chen, ... arXiv preprint arXiv:2308.09583, 2023 | 636 | 2023 |
| Fast convergence of regularized learning in games V Syrgkanis, A Agarwal, H Luo, RE Schapire Advances in Neural Information Processing Systems 28, 2015 | 358 | 2015 |
| Adaptive resource provisioning for the cloud using online bin packing W Song, Z Xiao, Q Chen, H Luo IEEE transactions on Computers 63 (11), 2647-2660, 2013 | 309 | 2013 |
| Corralling a band of bandit algorithms A Agarwal, H Luo, B Neyshabur, RE Schapire Conference on Learning Theory, 12-38, 2017 | 226 | 2017 |
| More adaptive algorithms for adversarial bandits CY Wei, H Luo Conference On Learning Theory, 1263-1291, 2018 | 221 | 2018 |
| Variance-reduced and projection-free stochastic optimization E Hazan, H Luo International Conference on Machine Learning, 1263-1271, 2016 | 213 | 2016 |
| Learning adversarial markov decision processes with bandit feedback and unknown transition C Jin, T Jin, H Luo, S Sra, T Yu International Conference on Machine Learning, 4860-4869, 2020 | 204* | 2020 |
| Achieving all with no parameters: Adanormalhedge H Luo, RE Schapire Conference on Learning Theory, 1286-1304, 2015 | 184 | 2015 |
| Practical contextual bandits with regression oracles D Foster, A Agarwal, M Dudík, H Luo, R Schapire International Conference on Machine Learning, 1539-1548, 2018 | 180 | 2018 |
| Linear last-iterate convergence in constrained saddle-point optimization CY Wei, CW Lee, M Zhang, H Luo arXiv preprint arXiv:2006.09517, 2020 | 173 | 2020 |
| 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 | 165 | 2019 |
| Non-stationary reinforcement learning without prior knowledge: An optimal black-box approach CY Wei, H Luo Conference on learning theory, 4300-4354, 2021 | 162 | 2021 |
| Efficient Contextual Bandits in Non-stationary Worlds H Luo, CY Wei, A Agarwal, J Langford arXiv preprint arXiv:1708.01799, 2017 | 160 | 2017 |
| 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 | 147 | 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 | 131 | 2021 |
| Model selection for contextual bandits DJ Foster, A Krishnamurthy, H Luo Advances in Neural Information Processing Systems 32, 2019 | 129 | 2019 |
| Efficient second order online learning by sketching H Luo, A Agarwal, N Cesa-Bianchi, J Langford Advances in Neural Information Processing Systems 29, 2016 | 124 | 2016 |
| Logistic regression: The importance of being improper DJ Foster, S Kale, H Luo, M Mohri, K Sridharan Conference on learning theory, 167-208, 2018 | 121 | 2018 |
| Beating stochastic and adversarial semi-bandits optimally and simultaneously J Zimmert, H Luo, CY Wei International Conference on Machine Learning, 7683-7692, 2019 | 112 | 2019 |
| Optimal and adaptive algorithms for online boosting A Beygelzimer, S Kale, H Luo International Conference on Machine Learning, 2323-2331, 2015 | 104 | 2015 |
Robert SchapireMicrosoft ResearchVerified email at microsoft.com
