| Bellman-consistent pessimism for offline reinforcement learning T Xie, CA Cheng, N Jiang, P Mineiro, A Agarwal Advances in neural information processing systems 34, 6683-6694, 2021 | 309 | 2021 |
| Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling T Xie, Y Ma, YX Wang Advances in Neural Information Processing Systems, 9665-9675, 2019 | 199 | 2019 |
| Policy finetuning: Bridging sample-efficient offline and online reinforcement learning T Xie, N Jiang, H Wang, C Xiong, Y Bai Advances in neural information processing systems 34, 27395-27407, 2021 | 192 | 2021 |
| Adversarially trained actor critic for offline reinforcement learning CA Cheng, T Xie, N Jiang, A Agarwal International Conference on Machine Learning, 3852-3878, 2022 | 153 | 2022 |
| Batch value-function approximation with only realizability T Xie, N Jiang International Conference on Machine Learning, 11404-11413, 2021 | 133 | 2021 |
| Provably efficient q-learning with low switching cost Y Bai, T Xie, N Jiang, YX Wang Advances in Neural Information Processing Systems, 8004-8013, 2019 | 121 | 2019 |
| Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison T Xie, N Jiang Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence …, 2020 | 113 | 2020 |
| Interpretable preferences via multi-objective reward modeling and mixture-of-experts H Wang, W Xiong, T Xie, H Zhao, T Zhang arXiv preprint arXiv:2406.12845, 2024 | 99 | 2024 |
| Direct nash optimization: Teaching language models to self-improve with general preferences C Rosset, CA Cheng, A Mitra, M Santacroce, A Awadallah, T Xie arXiv preprint arXiv:2404.03715, 2024 | 92 | 2024 |
| The role of coverage in online reinforcement learning T Xie, DJ Foster, Y Bai, N Jiang, SM Kakade arXiv preprint arXiv:2210.04157, 2022 | 86 | 2022 |
| Preference fine-tuning of llms should leverage suboptimal, on-policy data F Tajwar, A Singh, A Sharma, R Rafailov, J Schneider, T Xie, S Ermon, ... arXiv preprint arXiv:2404.14367, 2024 | 74 | 2024 |
| Finite sample analysis of minimax offline reinforcement learning: Completeness, fast rates and first-order efficiency M Uehara, M Imaizumi, N Jiang, N Kallus, W Sun, T Xie arXiv preprint arXiv:2102.02981, 2021 | 69 | 2021 |
| A Block Coordinate Ascent Algorithm for Mean-Variance Optimization T Xie, B Liu, Y Xu, M Ghavamzadeh, Y Chow, D Lyu, D Yoon Advances in Neural Information Processing Systems, 1073-1083, 2018 | 40 | 2018 |
| Adversarial model for offline reinforcement learning M Bhardwaj, T Xie, B Boots, N Jiang, CA Cheng Advances in Neural Information Processing Systems 36, 1245-1269, 2023 | 37 | 2023 |
| Exploratory preference optimization: Harnessing implicit q*-approximation for sample-efficient rlhf T Xie, DJ Foster, A Krishnamurthy, C Rosset, A Awadallah, A Rakhlin arXiv preprint arXiv:2405.21046, 2024 | 34 | 2024 |
| A variant of the wang-foster-kakade lower bound for the discounted setting P Amortila, N Jiang, T Xie arXiv preprint arXiv:2011.01075, 2020 | 25 | 2020 |
| Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization A Huang, W Zhan, T Xie, JD Lee, W Sun, A Krishnamurthy, DJ Foster arXiv preprint arXiv:2407.13399, 2024 | 16* | 2024 |
| Harnessing density ratios for online reinforcement learning P Amortila, DJ Foster, N Jiang, A Sekhari, T Xie arXiv preprint arXiv:2401.09681, 2024 | 15 | 2024 |
| Countercurate: Enhancing physical and semantic visio-linguistic compositional reasoning via counterfactual examples J Zhang, M Cai, T Xie, YJ Lee arXiv preprint arXiv:2402.13254, 2024 | 11 | 2024 |
| Interaction-Grounded Learning T Xie, J Langford, P Mineiro, I Momennejad International Conference on Machine Learning, 11414-11423, 2021 | 11 | 2021 |
Nan JiangAssociate Professor of Computer Science, UIUCایمیل تأیید شده در illinois.edu
