| Learning action representations for reinforcement learning Y Chandak, G Theocharous, J Kostas, S Jordan, P Thomas International conference on machine learning, 941-950, 2019 | 181 | 2019 |
| Optimizing for the future in non-stationary mdps Y Chandak, G Theocharous, S Shankar, M White, S Mahadevan, ... International Conference on Machine Learning, 1414-1425, 2020 | 63 | 2020 |
| Evaluating the performance of reinforcement learning algorithms S Jordan, Y Chandak, D Cohen, M Zhang, P Thomas International Conference on Machine Learning, 4962-4973, 2020 | 60 | 2020 |
| Universal off-policy evaluation Y Chandak, S Niekum, B da Silva, E Learned-Miller, E Brunskill, ... Advances in Neural Information Processing Systems 34, 27475-27490, 2021 | 48 | 2021 |
| Lifelong learning with a changing action set Y Chandak, G Theocharous, C Nota, P Thomas Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3373-3380, 2020 | 31 | 2020 |
| Towards safe policy improvement for non-stationary MDPs Y Chandak, S Jordan, G Theocharous, M White, PS Thomas Advances in Neural Information Processing Systems 33, 9156-9168, 2020 | 26 | 2020 |
| Understanding self-predictive learning for reinforcement learning Y Tang, ZD Guo, PH Richemond, BA Pires, Y Chandak, R Munos, ... International Conference on Machine Learning, 33632-33656, 2023 | 21 | 2023 |
| Supervised pretraining can learn in-context reinforcement learning J Lee, A Xie, A Pacchiano, Y Chandak, C Finn, O Nachum, E Brunskill Advances in Neural Information Processing Systems 36, 2024 | 19 | 2024 |
| Reinforcement learning for strategic recommendations G Theocharous, Y Chandak, PS Thomas, F de Nijs arXiv preprint arXiv:2009.07346, 2020 | 12 | 2020 |
| Fusion graph convolutional networks P Vijayan, Y Chandak, MM Khapra, S Parthasarathy, B Ravindran arXiv preprint arXiv:1805.12528, 2018 | 11 | 2018 |
| Reinforcement learning when all actions are not always available Y Chandak, G Theocharous, B Metevier, P Thomas Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 3381-3388, 2020 | 8 | 2020 |
| Sope: Spectrum of off-policy estimators C Yuan, Y Chandak, S Giguere, PS Thomas, S Niekum Advances in Neural Information Processing Systems 34, 18958-18969, 2021 | 6 | 2021 |
| High-confidence off-policy (or counterfactual) variance estimation Y Chandak, S Shankar, PS Thomas Proceedings of the AAAI Conference on Artificial Intelligence 35 (8), 6939-6947, 2021 | 6 | 2021 |
| Factored DRO: Factored distributionally robust policies for contextual bandits T Mu, Y Chandak, TB Hashimoto, E Brunskill Advances in Neural Information Processing Systems 35, 8318-8331, 2022 | 5 | 2022 |
| Off-policy evaluation for action-dependent non-stationary environments Y Chandak, S Shankar, N Bastian, B da Silva, E Brunskill, PS Thomas Advances in Neural Information Processing Systems 35, 9217-9232, 2022 | 4 | 2022 |
| HOPF: higher order propagation framework for deep collective classification P Vijayan, Y Chandak, MM Khapra, S Parthasarathy, B Ravindran arXiv preprint arXiv:1805.12421, 2018 | 4 | 2018 |
| High confidence generalization for reinforcement learning J Kostas, Y Chandak, SM Jordan, G Theocharous, P Thomas International Conference on Machine Learning, 5764-5773, 2021 | 3 | 2021 |
| Generating and providing proposed digital actions in high-dimensional action spaces using reinforcement learning models Y Chandak, G Theocharous US Patent App. 16/261,092, 2020 | 3 | 2020 |
| On optimizing human-machine task assignments A Veit, M Wilber, R Vaish, S Belongie, J Davis, V Anand, A Aviral, ... arXiv preprint arXiv:1509.07543, 2015 | 3 | 2015 |
| On optimizing interventions in shared autonomy W Tan, D Koleczek, S Pradhan, N Perello, V Chettiar, V Rohra, A Rajaram, ... Proceedings of the AAAI Conference on Artificial Intelligence 36 (5), 5341-5349, 2022 | 2 | 2022 |
Philip ThomasUniversity of Massachusetts AmherstVerified email at cs.umass.edu
