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2020 – today
- 2024
[c16]Jianrui Zhang, Mu Cai, Tengyang Xie, Yong Jae Lee:
CounterCurate: Enhancing Physical and Semantic Visio-Linguistic Compositional Reasoning via Counterfactual Examples. ACL (Findings) 2024: 15481-15495- [c15]Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie:
Harnessing Density Ratios for Online Reinforcement Learning. ICLR 2024 - [c14]Dipendra Misra, Akanksha Saran, Tengyang Xie, Alex Lamb, John Langford:
Towards Principled Representation Learning from Videos for Reinforcement Learning. ICLR 2024 - [c13]Fahim Tajwar, Anikait Singh, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, Aviral Kumar:
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data. ICML 2024 - [i25]Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie:
Harnessing Density Ratios for Online Reinforcement Learning. CoRR abs/2401.09681 (2024) - [i24]Jianrui Zhang, Mu Cai, Tengyang Xie, Yong Jae Lee:
CounterCurate: Enhancing Physical and Semantic Visio-Linguistic Compositional Reasoning via Counterfactual Examples. CoRR abs/2402.13254 (2024) - [i23]Dipendra Misra, Akanksha Saran, Tengyang Xie, Alex Lamb, John Langford:
Towards Principled Representation Learning from Videos for Reinforcement Learning. CoRR abs/2403.13765 (2024) - [i22]Corby Rosset, Ching-An Cheng, Arindam Mitra, Michael Santacroce, Ahmed Awadallah, Tengyang Xie:
Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences. CoRR abs/2404.03715 (2024) - [i21]Fahim Tajwar, Anikait Singh, Archit Sharma, Rafael Rafailov, Jeff Schneider, Tengyang Xie, Stefano Ermon, Chelsea Finn, Aviral Kumar:
Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data. CoRR abs/2404.14367 (2024) - [i20]Tengyang Xie, Dylan J. Foster, Akshay Krishnamurthy, Corby Rosset, Ahmed Awadallah, Alexander Rakhlin:
Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF. CoRR abs/2405.21046 (2024) - [i19]Xiang Ji, Sanjeev Kulkarni, Mengdi Wang, Tengyang Xie:
Self-Play with Adversarial Critic: Provable and Scalable Offline Alignment for Language Models. CoRR abs/2406.04274 (2024) - [i18]Haoxiang Wang, Wei Xiong, Tengyang Xie, Han Zhao, Tong Zhang:
Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts. CoRR abs/2406.12845 (2024) - [i17]Audrey Huang, Wenhao Zhan, Tengyang Xie, Jason D. Lee, Wen Sun, Akshay Krishnamurthy, Dylan J. Foster:
Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization. CoRR abs/2407.13399 (2024) - 2023
- [c12]Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang, Sham M. Kakade:
The Role of Coverage in Online Reinforcement Learning. ICLR 2023 - [c11]Mohak Bhardwaj, Tengyang Xie, Byron Boots, Nan Jiang, Ching-An Cheng:
Adversarial Model for Offline Reinforcement Learning. NeurIPS 2023 - [i16]Mohak Bhardwaj, Tengyang Xie, Byron Boots, Nan Jiang, Ching-An Cheng:
Adversarial Model for Offline Reinforcement Learning. CoRR abs/2302.11048 (2023) - 2022
- [c10]Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal:
Adversarially Trained Actor Critic for Offline Reinforcement Learning. ICML 2022: 3852-3878 - [c9]Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan P. Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford:
Interaction-Grounded Learning with Action-Inclusive Feedback. NeurIPS 2022 - [i15]Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal:
Adversarially Trained Actor Critic for Offline Reinforcement Learning. CoRR abs/2202.02446 (2022) - [i14]Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan P. Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford:
Interaction-Grounded Learning with Action-inclusive Feedback. CoRR abs/2206.08364 (2022) - [i13]Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang, Sham M. Kakade:
The Role of Coverage in Online Reinforcement Learning. CoRR abs/2210.04157 (2022) - [i12]Tengyang Xie, Mohak Bhardwaj, Nan Jiang, Ching-An Cheng:
ARMOR: A Model-based Framework for Improving Arbitrary Baseline Policies with Offline Data. CoRR abs/2211.04538 (2022) - 2021
- [c8]Tengyang Xie, Nan Jiang:
Batch Value-function Approximation with Only Realizability. ICML 2021: 11404-11413 - [c7]Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad:
Interaction-Grounded Learning. ICML 2021: 11414-11423 - [c6]Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal:
Bellman-consistent Pessimism for Offline Reinforcement Learning. NeurIPS 2021: 6683-6694 - [c5]Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, Yu Bai:
Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning. NeurIPS 2021: 27395-27407 - [i11]Masatoshi Uehara, Masaaki Imaizumi, Nan Jiang, Nathan Kallus, Wen Sun, Tengyang Xie:
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency. CoRR abs/2102.02981 (2021) - [i10]Tengyang Xie, John Langford, Paul Mineiro, Ida Momennejad:
Interaction-Grounded Learning. CoRR abs/2106.04887 (2021) - [i9]Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, Yu Bai:
Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning. CoRR abs/2106.04895 (2021) - [i8]Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal:
Bellman-consistent Pessimism for Offline Reinforcement Learning. CoRR abs/2106.06926 (2021) - 2020
- [c4]Tengyang Xie, Nan Jiang:
Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison. UAI 2020: 550-559 - [i7]Tengyang Xie, Nan Jiang:
Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison. CoRR abs/2003.03924 (2020) - [i6]Tengyang Xie, Nan Jiang:
Batch Value-function Approximation with Only Realizability. CoRR abs/2008.04990 (2020) - [i5]Philip Amortila, Nan Jiang, Tengyang Xie:
A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting. CoRR abs/2011.01075 (2020)
2010 – 2019
- 2019
- [c3]Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang:
Provably Efficient Q-Learning with Low Switching Cost. NeurIPS 2019: 8002-8011 - [c2]Tengyang Xie, Yifei Ma, Yu-Xiang Wang:
Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling. NeurIPS 2019: 9665-9675 - [i4]Tengyang Xie, Philip S. Thomas, Gerome Miklau:
Privacy Preserving Off-Policy Evaluation. CoRR abs/1902.00174 (2019) - [i3]Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang:
Provably Efficient Q-Learning with Low Switching Cost. CoRR abs/1905.12849 (2019) - [i2]Tengyang Xie, Yifei Ma, Yu-Xiang Wang:
Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling. CoRR abs/1906.03393 (2019) - 2018
- [c1]Tengyang Xie, Bo Liu, Yangyang Xu, Mohammad Ghavamzadeh, Yinlam Chow, Daoming Lyu
, Daesub Yoon:
A Block Coordinate Ascent Algorithm for Mean-Variance Optimization. NeurIPS 2018: 1073-1083 - [i1]Bo Liu, Tengyang Xie, Yangyang Xu, Mohammad Ghavamzadeh, Yinlam Chow, Daoming Lyu, Daesub Yoon:
A Block Coordinate Ascent Algorithm for Mean-Variance Optimization. CoRR abs/1809.02292 (2018)
Coauthor Index
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