| A lyapunov-based approach to safe reinforcement learning Y Chow, O Nachum, E Duenez-Guzman, M Ghavamzadeh arXiv preprint arXiv:1805.07708, 2018 | 148 | 2018 |
| Risk-sensitive and robust decision-making: a cvar optimization approach Y Chow, A Tamar, S Mannor, M Pavone arXiv preprint arXiv:1506.02188, 2015 | 137 | 2015 |
| Risk-constrained reinforcement learning with percentile risk criteria Y Chow, M Ghavamzadeh, L Janson, M Pavone The Journal of Machine Learning Research 18 (1), 6070-6120, 2017 | 127 | 2017 |
| More robust doubly robust off-policy evaluation M Farajtabar, Y Chow, M Ghavamzadeh International Conference on Machine Learning, 1447-1456, 2018 | 100 | 2018 |
| Algorithms for CVaR optimization in MDPs Y Chow, M Ghavamzadeh arXiv preprint arXiv:1406.3339, 2014 | 91 | 2014 |
| Safe policy improvement by minimizing robust baseline regret M Ghavamzadeh, M Petrik, Y Chow Advances in Neural Information Processing Systems 29, 2298-2306, 2016 | 86 | 2016 |
| Dualdice: Behavior-agnostic estimation of discounted stationary distribution corrections O Nachum, Y Chow, B Dai, L Li arXiv preprint arXiv:1906.04733, 2019 | 64 | 2019 |
| Online modified greedy algorithm for storage control under uncertainty J Qin, Y Chow, J Yang, R Rajagopal IEEE Transactions on Power Systems 31 (3), 1729-1743, 2015 | 50 | 2015 |
| Lyapunov-based safe policy optimization for continuous control Y Chow, O Nachum, A Faust, E Duenez-Guzman, M Ghavamzadeh arXiv preprint arXiv:1901.10031, 2019 | 47 | 2019 |
| Policy gradient for coherent risk measures A Tamar, Y Chow, M Ghavamzadeh, S Mannor arXiv preprint arXiv:1502.03919, 2015 | 45 | 2015 |
| Algaedice: Policy gradient from arbitrary experience O Nachum, B Dai, I Kostrikov, Y Chow, L Li, D Schuurmans arXiv preprint arXiv:1912.02074, 2019 | 33 | 2019 |
| Distributed online modified greedy algorithm for networked storage operation under uncertainty J Qin, Y Chow, J Yang, R Rajagopal IEEE Transactions on Smart Grid 7 (2), 1106-1118, 2015 | 30 | 2015 |
| Sequential decision making with coherent risk A Tamar, Y Chow, M Ghavamzadeh, S Mannor IEEE Transactions on Automatic Control 62 (7), 3323-3338, 2016 | 29 | 2016 |
| Weighted SGD for ℓp regression with randomized preconditioning J Yang, YL Chow, C Ré, MW Mahoney The Journal of Machine Learning Research 18 (1), 7811-7853, 2017 | 28 | 2017 |
| A framework for time-consistent, risk-averse model predictive control: Theory and algorithms YL Chow, M Pavone 2014 American Control Conference, 4204-4211, 2014 | 24 | 2014 |
| A framework for time-consistent, risk-sensitive model predictive control: Theory and algorithms S Singh, Y Chow, A Majumdar, M Pavone IEEE Transactions on Automatic Control 64 (7), 2905-2912, 2018 | 23 | 2018 |
| Stochastic optimal control with dynamic, time-consistent risk constraints YL Chow, M Pavone 2013 American Control Conference, 390-395, 2013 | 17 | 2013 |
| Path consistency learning in tsallis entropy regularized mdps Y Chow, O Nachum, M Ghavamzadeh International Conference on Machine Learning, 979-988, 2018 | 16 | 2018 |
| Trading safety versus performance: Rapid deployment of robotic swarms with robust performance constraints YL Chow, M Pavone, BM Sadler, S Carpin Journal of Dynamic Systems, Measurement, and Control 137 (3), 2015 | 16 | 2015 |
| CAQL: Continuous action Q-learning M Ryu, Y Chow, R Anderson, C Tjandraatmadja, C Boutilier arXiv preprint arXiv:1909.12397, 2019 | 15 | 2019 |
Marco PavoneAssociate Professor of Aeronautics and Astronautics, Stanford UniversityVerified email at stanford.edu
