| Learning action representations for reinforcement learning Y Chandak, G Theocharous, J Kostas, S Jordan, P Thomas International conference on machine learning, 941-950, 2019 | 221 | 2019 |
| 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 | 79 | 2020 |
| 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 | 79 | 2020 |
| 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, 43057-43083, 2023 | 77 | 2023 |
| 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 | 61 | 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 | 36 | 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 | 34 | 2023 |
| 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 | 32 | 2020 |
| The GPT surprise: offering large language model chat in a massive coding class reduced engagement but increased adopters exam performances A Nie, Y Chandak, M Suzara, M Ali, J Woodrow, M Peng, M Sahami, ... arXiv preprint arXiv:2407.09975, 2024 | 22 | 2024 |
| Behavior alignment via reward function optimization D Gupta, Y Chandak, S Jordan, PS Thomas, B C da Silva Advances in Neural Information Processing Systems 36, 52759-52791, 2023 | 13 | 2023 |
| Reinforcement learning for strategic recommendations G Theocharous, Y Chandak, PS Thomas, F de Nijs arXiv preprint arXiv:2009.07346, 2020 | 12 | 2020 |
| 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 | 10 | 2020 |
| Fusion graph convolutional networks P Vijayan, Y Chandak, MM Khapra, S Parthasarathy, B Ravindran arXiv preprint arXiv:1805.12528, 2018 | 10 | 2018 |
| 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 | 8 | 2022 |
| 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 | 8 | 2021 |
| Adaptive instrument design for indirect experiments Y Chandak, S Shankar, V Syrgkanis, E Brunskill arXiv preprint arXiv:2312.02438, 2023 | 6 | 2023 |
| 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 | 6 | 2022 |
| 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 | 6 | 2022 |
| 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 |
| Representations and exploration for deep reinforcement learning using singular value decomposition Y Chandak, S Thakoor, ZD Guo, Y Tang, R Munos, W Dabney, DL Borsa International Conference on Machine Learning, 4009-4034, 2023 | 5 | 2023 |
Philip ThomasUniversity of Massachusetts AmherstVerified email at cs.umass.edu
