| Provably good batch reinforcement learning without great exploration Y Liu, A Swaminathan, A Agarwal, E Brunskill Advances in Neural Information Processing Systems 33, 1264–1274, 2020 | 231 | 2020 |
| Off-Policy Policy Gradient with Stationary Distribution Correction Y Liu, A Swaminathan, A Agarwal, E Brunskill Proceedings of The 35th Uncertainty in Artificial Intelligence Conference …, 2019 | 187* | 2019 |
| Representation balancing mdps for off-policy policy evaluation Y Liu, O Gottesman, A Raghu, M Komorowski, A Faisal, F Doshi-Velez, ... Advances in Neural Information Processing Systems 31, 2644--2653, 2018 | 83 | 2018 |
| Interpretable off-policy evaluation in reinforcement learning by highlighting influential transitions O Gottesman, J Futoma, Y Liu, S Parbhoo, L Celi, E Brunskill, ... International Conference on Machine Learning, 3658-3667, 2020 | 66 | 2020 |
| Behaviour policy estimation in off-policy policy evaluation: Calibration matters A Raghu, O Gottesman, Y Liu, M Komorowski, A Faisal, F Doshi-Velez, ... arXiv preprint arXiv:1807.01066, 2018 | 46 | 2018 |
| Understanding the curse of horizon in off-policy evaluation via conditional importance sampling Y Liu, PL Bacon, E Brunskill International Conference on Machine Learning, 6184-6193, 2020 | 43 | 2020 |
| Combining parametric and nonparametric models for off-policy evaluation O Gottesman, Y Liu, S Sussex, E Brunskill, F Doshi-Velez In International Conference on Machine Learning, 2366-2375, 2019 | 36 | 2019 |
| When Simple Exploration is Sample Efficient: Identifying Sufficient Conditions for Random Exploration to Yield PAC RL Algorithms Y Liu, E Brunskill The 14th European Workshop on Reinforcement Learning, 2018 | 28 | 2018 |
| Pac continuous state online multitask reinforcement learning with identification Y Liu, Z Guo, E Brunskill Proceedings of the 2016 International Conference on Autonomous Agents …, 2016 | 22 | 2016 |
| Reinforcement learning tutor better supported lower performers in a math task S Ruan, A Nie, W Steenbergen, J He, JQ Zhang, M Guo, Y Liu, ... Machine Learning, 1-26, 2024 | 20 | 2024 |
| Tail: Task-specific adapters for imitation learning with large pretrained models Z Liu, J Zhang, K Asadi, Y Liu, D Zhao, S Sabach, R Fakoor arXiv preprint arXiv:2310.05905, 2023 | 14 | 2023 |
| All-action policy gradient methods: A numerical integration approach B Petit, L Amdahl-Culleton, Y Liu, J Smith, PL Bacon arXiv preprint arXiv:1910.09093, 2019 | 9 | 2019 |
| Td convergence: An optimization perspective K Asadi, S Sabach, Y Liu, O Gottesman, R Fakoor Advances in Neural Information Processing Systems 36, 2024 | 5 | 2024 |
| Budgeting counterfactual for offline RL Y Liu, P Chaudhari, R Fakoor Advances in Neural Information Processing Systems 36, 2024 | 4 | 2024 |
| Offline policy optimization with eligible actions Y Liu, Y Flet-Berliac, E Brunskill Uncertainty in Artificial Intelligence, 1253-1263, 2022 | 4 | 2022 |
| Agentoccam: A simple yet strong baseline for llm-based web agents K Yang, Y Liu, S Chaudhary, R Fakoor, P Chaudhari, G Karypis, ... arXiv preprint arXiv:2410.13825, 2024 | 3 | 2024 |
| Nonlinear Dimensionality Reduction by Local Orthogonality Preserving Alignment T Lin, Y Liu, B Wang, LW Wang, HB Zha Journal of Computer Science and Technology 31 (3), 512-524, 2016 | 3* | 2016 |
| Provably sample-efficient RL with side information about latent dynamics Y Liu, D Misra, M Dudík, RE Schapire Advances in Neural Information Processing Systems 35, 33482-33493, 2022 | 2 | 2022 |
| EXTRACT: Efficient Policy Learning by Extracting Transferable Robot Skills from Offline Data J Zhang, M Heo, Z Liu, E Biyik, JJ Lim, Y Liu, R Fakoor arXiv preprint arXiv:2406.17768, 2024 | 1 | 2024 |
| Learning the Target Network in Function Space K Asadi, Y Liu, S Sabach, M Yin, R Fakoor arXiv preprint arXiv:2406.01838, 2024 | 1 | 2024 |
Emma BrunskillAssociate Professor of Computer Science, Stanford UniversityPotvrđena adresa e-pošte na cs.stanford.edu
