A Block Coordinate Ascent Algorithm for Mean-Variance Optimization. - Tengyang Xie; Bo Liu 006; Yangyang Xu; Mohammad Ghavamzadeh; Yinlam Chow; Daoming Lyu; Daesub Yoon (NeurIPS, 2018)

Provably Efficient Q-Learning with Low Switching Cost. - Yu Bai 017; Tengyang Xie; Nan Jiang 008; Yu-Xiang Wang 003 (NeurIPS, 2019)

Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling. - Tengyang Xie; Yifei Ma; Yu-Xiang Wang 003 (NeurIPS, 2019)

Privacy Preserving Off-Policy Evaluation. - Tengyang Xie; Philip S. Thomas; Gerome Miklau (CoRR, 2019)

Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling. - Tengyang Xie; Yifei Ma; Yu-Xiang Wang 003 (CoRR, 2019)

Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison. - Tengyang Xie; Nan Jiang 008 (UAI, 2020)

Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison. - Tengyang Xie; Nan Jiang 008 (CoRR, 2020)

A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting. - Philip Amortila; Nan Jiang 008; Tengyang Xie (CoRR, 2020)

Interaction-Grounded Learning. - Tengyang Xie; John Langford 001; Paul Mineiro; Ida Momennejad (ICML, 2021)

Batch Value-function Approximation with Only Realizability. - Tengyang Xie; Nan Jiang 008 (ICML, 2021)

Bellman-consistent Pessimism for Offline Reinforcement Learning. - Tengyang Xie; Ching-An Cheng; Nan Jiang 008; Paul Mineiro; Alekh Agarwal (NeurIPS, 2021)

Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning. - Tengyang Xie; Nan Jiang 008; Huan Wang 014; Caiming Xiong; Yu Bai 017 (NeurIPS, 2021)

Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency. - Masatoshi Uehara; Masaaki Imaizumi; Nan Jiang 008; Nathan Kallus; Wen Sun 002; Tengyang Xie (CoRR, 2021)

Adversarially Trained Actor Critic for Offline Reinforcement Learning. - Ching-An Cheng; Tengyang Xie; Nan Jiang 008; Alekh Agarwal (ICML, 2022)

Interaction-Grounded Learning with Action-Inclusive Feedback. - Tengyang Xie; Akanksha Saran; Dylan J. Foster; Lekan P. Molu; Ida Momennejad; Nan Jiang 008; Paul Mineiro; John Langford 001 (NeurIPS, 2022)

ARMOR: A Model-based Framework for Improving Arbitrary Baseline Policies with Offline Data. - Tengyang Xie; Mohak Bhardwaj; Nan Jiang 008; Ching-An Cheng (CoRR, 2022)

The Role of Coverage in Online Reinforcement Learning. - Tengyang Xie; Dylan J. Foster; Yu Bai 017; Nan Jiang 008; Sham M. Kakade (ICLR, 2023)

Adversarial Model for Offline Reinforcement Learning. - Mohak Bhardwaj; Tengyang Xie; Byron Boots; Nan Jiang 008; Ching-An Cheng (NeurIPS, 2023)

CounterCurate: Enhancing Physical and Semantic Visio-Linguistic Compositional Reasoning via Counterfactual Examples. - Jianrui Zhang; Mu Cai; Tengyang Xie; Yong Jae Lee (ACL (Findings), 2024)

Interpretable Preferences via Multi-Objective Reward Modeling and Mixture-of-Experts. - Haoxiang Wang 003; Wei Xiong 015; Tengyang Xie; Han Zhao 002; Tong Zhang 001 (EMNLP (Findings), 2024)

Harnessing Density Ratios for Online Reinforcement Learning. - Philip Amortila; Dylan J. Foster; Nan Jiang 008; Ayush Sekhari; Tengyang Xie (ICLR, 2024)

Towards Principled Representation Learning from Videos for Reinforcement Learning. - Dipendra Misra; Akanksha Saran; Tengyang Xie; Alex Lamb; John Langford 001 (ICLR, 2024)

Preference Fine-Tuning of LLMs Should Leverage Suboptimal, On-Policy Data. - Fahim Tajwar; Anikait Singh; Archit Sharma; Rafael Rafailov; Jeff Schneider 001; Tengyang Xie; Stefano Ermon; Chelsea Finn; Aviral Kumar (ICML, 2024)

Direct Nash Optimization: Teaching Language Models to Self-Improve with General Preferences. - Corby Rosset; Ching-An Cheng; Arindam Mitra; Michael Santacroce; Ahmed Awadallah 001; Tengyang Xie (CoRR, 2024)

Self-Play with Adversarial Critic: Provable and Scalable Offline Alignment for Language Models. - Xiang Ji; Sanjeev Kulkarni 001; Mengdi Wang; Tengyang Xie (CoRR, 2024)

Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization. - Audrey Huang; Wenhao Zhan; Tengyang Xie; Jason D. Lee; Wen Sun 002; Akshay Krishnamurthy; Dylan J. Foster (ICLR, 2025)

Exploratory Preference Optimization: Harnessing Implicit Q*-Approximation for Sample-Efficient RLHF. - Tengyang Xie; Dylan J. Foster; Akshay Krishnamurthy; Corby Rosset; Ahmed Hassan Awadallah; Alexander Rakhlin (ICLR, 2025)

Do We Need to Verify Step by Step? Rethinking Process Supervision from a Theoretical Perspective. - Zeyu Jia; Alexander Rakhlin; Tengyang Xie (CoRR, 2025)

Trajectory Bellman Residual Minimization: A Simple Value-Based Method for LLM Reasoning. - Yurun Yuan; Fan Chen; Zeyu Jia; Alexander Rakhlin; Tengyang Xie (CoRR, 2025)

Outcome-Based Online Reinforcement Learning: Algorithms and Fundamental Limits. - Fan Chen; Zeyu Jia; Alexander Rakhlin; Tengyang Xie (CoRR, 2025)

Reinforce LLM Reasoning through Multi-Agent Reflection. - Yurun Yuan; Tengyang Xie (CoRR, 2025)