| A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges M Abdar, F Pourpanah, S Hussain, D Rezazadegan, L Liu, ... Information Fusion, 2021 | 2467 | 2021 |
| Natural Actor–critic Algorithms S Bhatnagar, RS Sutton, M Ghavamzadeh, M Lee Automatica 45 (11), 2471-2482, 2009 | 1121* | 2009 |
| A Lyapunov-based Approach to Safe Reinforcement Learning Y Chow, O Nachum, E Duenez-Guzman, M Ghavamzadeh Neural Information Processing Systems, 8103-8112, 2018 | 633 | 2018 |
| Risk-constrained Reinforcement Learning with Percentile Risk Criteria Y Chow, M Ghavamzadeh, L Janson, M Pavone Journal of Machine Learning Research (JMLR) 18, 6070-6120, 2017 | 630 | 2017 |
| Bayesian Reinforcement Learning: A Survey M Ghavamzadeh, S Mannor, J Pineau, A Tamar Foundations and Trends in Machine Learning 8 (5-6), 359-483, 2015 | 594 | 2015 |
| Algorithms for CVaR Optimization in MDPs Y Chow, M Ghavamzadeh Advances in Neural Information Processing Systems, 3509-3517, 2014 | 407 | 2014 |
| Best Arm Identification: A Unified Approach to Fixed Budget and Fixed Confidence V Gabillon, M Ghavamzadeh, A Lazaric Neural Information Processing Systems, 3221-3229, 2012 | 370 | 2012 |
| High-confidence Off-policy Evaluation P Thomas, G Theocharous, M Ghavamzadeh AAAI, 3000-3006, 2015 | 339 | 2015 |
| Actor-Critic Algorithms for Risk-sensitive MDPs LA Prashanth, M Ghavamzadeh Neural Information Processing Systems, 252-260, 2013 | 338* | 2013 |
| Safe Policy Learning for Continuous Control Y Chow, O Nachum, A Faust, E Duenez-Guzman, M Ghavamzadeh Conference on Robot Learning (CoRL), 2020 | 305* | 2020 |
| More Robust Doubly Robust Off-policy Evaluation M Farajtabar, Y Chow, M Ghavamzadeh ICML, 1447-1456, 2018 | 294 | 2018 |
| Aligning text-to-image models using human feedback K Lee, H Liu, M Ryu, O Watkins, Y Du, C Boutilier, P Abbeel, ... arXiv preprint arXiv:2302.12192, 2023 | 232 | 2023 |
| High Confidence Policy Improvement P Thomas, G Theocharous, M Ghavamzadeh ICML, 2380-2388, 2015 | 225 | 2015 |
| Speedy Q-learning M Ghavamzadeh, H Kappen, M Azar, R Munos Neural Information Processing Systems 24, 2411-2419, 2011 | 225* | 2011 |
| Benchmarking Batch Deep Reinforcement Learning Algorithms S Fujimoto, E Conti, M Ghavamzadeh, J Pineau arXiv preprint arXiv:1910.01708, 2019 | 219 | 2019 |
| Personalized Ad Recommendation Systems for Life-time Value Optimization with Guarantees G Theocharous, PS Thomas, M Ghavamzadeh IJCAI, 1806-1812, 2015 | 208* | 2015 |
| Supervised actor-critic reinforcement learning MT Rosenstein, AG Barto, J Si, A Barto, W Powell, D Wunsch Learning and approximate dynamic programming: scaling up to the real world …, 2004 | 202 | 2004 |
| Hierarchical Multi-agent Reinforcement Learning R Makar, S Mahadevan, M Ghavamzadeh International Conference on Autonomous Agents, 246-253, 2001 | 198 | 2001 |
| Reinforcement Learning for Fine-tuning Text-to-Image Diffusion Models Y Fan, O Watkins, Y Du, H Liu, M Ryu, C Boutilier, P Abbeel, ... Advances in Neural Information Processing Systems, 2024 | 185 | 2024 |
| Hierarchical Multi-agent Reinforcement Learning M Ghavamzadeh, S Mahadevan, R Makar Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS) 13 (2), 197-229, 2006 | 183 | 2006 |
Yinlam ChowResearch Scientist, Google Researchgoogle.com의 이메일 확인됨