Improving UCT planning via approximate homomorphisms. - Nan Jiang 008; Satinder Singh 001; Richard L. Lewis (AAMAS, 2014)

Spectral Learning of Predictive State Representations with Insufficient Statistics. - Alex Kulesza; Nan Jiang 008; Satinder Singh 001 (AAAI, 2015)

Low-Rank Spectral Learning with Weighted Loss Functions. - Alex Kulesza; Nan Jiang 008; Satinder Singh 001 (AISTATS, 2015)

Abstraction Selection in Model-based Reinforcement Learning. - Nan Jiang 008; Alex Kulesza; Satinder Singh 001 (ICML, 2015)

Improving Predictive State Representations via Gradient Descent. - Nan Jiang 008; Alex Kulesza; Satinder Singh 001 (AAAI, 2016)

The Dependence of Effective Planning Horizon on Model Accuracy. - Nan Jiang 008; Alex Kulesza; Satinder Singh 001; Richard L. Lewis (IJCAI, 2016)

On Structural Properties of MDPs that Bound Loss Due to Shallow Planning. - Nan Jiang 008; Satinder Singh 001; Ambuj Tewari (IJCAI, 2016)

Contextual Decision Processes with low Bellman rank are PAC-Learnable. - Nan Jiang 008; Akshay Krishnamurthy; Alekh Agarwal; John Langford 001; Robert E. Schapire (ICML, 2017)

Repeated Inverse Reinforcement Learning. - Kareem Amin 002; Nan Jiang 008; Satinder Singh 001 (NIPS, 2017)

Markov Decision Processes with Continuous Side Information. - Aditya Modi 002; Nan Jiang 008; Satinder Singh 001; Ambuj Tewari (ALT, 2018)

Open Problem: The Dependence of Sample Complexity Lower Bounds on Planning Horizon. - Nan Jiang 008; Alekh Agarwal (COLT, 2018)

Hierarchical Imitation and Reinforcement Learning. - Hoang Minh Le 002; Nan Jiang 008; Alekh Agarwal; Miroslav Dudík; Yisong Yue; Hal Daumé III (ICML, 2018)

On Oracle-Efficient PAC RL with Rich Observations. - Christoph Dann; Nan Jiang 008; Akshay Krishnamurthy; Alekh Agarwal; John Langford 001; Robert E. Schapire (NeurIPS, 2018)

Completing State Representations using Spectral Learning. - Nan Jiang 008; Alex Kulesza; Satinder Singh 001 (NeurIPS, 2018)

On Polynomial Time PAC Reinforcement Learning with Rich Observations. - Christoph Dann; Nan Jiang 008; Akshay Krishnamurthy; Alekh Agarwal; John Langford 001; Robert E. Schapire (CoRR, 2018)

Model-Based Reinforcement Learning in Contextual Decision Processes. - Wen Sun 002; Nan Jiang 008; Akshay Krishnamurthy; Alekh Agarwal; John Langford 001 (CoRR, 2018)

Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches. - Wen Sun 002; Nan Jiang 008; Akshay Krishnamurthy; Alekh Agarwal; John Langford 001 (COLT, 2019)

Information-Theoretic Considerations in Batch Reinforcement Learning. - Jinglin Chen; Nan Jiang 008 (ICML, 2019)

Provably efficient RL with Rich Observations via Latent State Decoding. - Simon S. Du; Akshay Krishnamurthy; Nan Jiang 008; Alekh Agarwal; Miroslav Dudík; John Langford 001 (ICML, 2019)

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

Minimax Weight and Q-Function Learning for Off-Policy Evaluation. - Masatoshi Uehara; Nan Jiang 008 (CoRR, 2019)

Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles. - Aditya Modi 002; Nan Jiang 008; Ambuj Tewari; Satinder Singh 001 (AISTATS, 2020)

From Importance Sampling to Doubly Robust Policy Gradient. - Jiawei Huang; Nan Jiang 008 (ICML, 2020)

Minimax Weight and Q-Function Learning for Off-Policy Evaluation. - Masatoshi Uehara; Jiawei Huang; Nan Jiang 008 (ICML, 2020)

Minimax Value Interval for Off-Policy Evaluation and Policy Optimization. - Nan Jiang 008; Jiawei Huang (NeurIPS, 2020)

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

Minimax Confidence Interval for Off-Policy Evaluation and Policy Optimization. - Nan Jiang 008; Jiawei Huang (CoRR, 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)

Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration. - Priyank Agrawal; Jinglin Chen; Nan Jiang 008 (AAAI, 2021)

Minimax Model Learning. - Cameron Voloshin; Nan Jiang 008; Yisong Yue (AISTATS, 2021)

On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function. - Gellért Weisz; Philip Amortila; Barnabás Janzer; Yasin Abbasi-Yadkori; Nan Jiang 008; Csaba Szepesvári (COLT, 2021)

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

Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning. - Cameron Voloshin; Hoang Minh Le 002; Nan Jiang 008; Yisong Yue (NeurIPS Datasets and Benchmarks, 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)

A Minimax Learning Approach to Off-Policy Evaluation in Partially Observable Markov Decision Processes. - Chengchun Shi; Masatoshi Uehara; Nan Jiang 008 (CoRR, 2021)

On the Convergence Rate of Off-Policy Policy Optimization Methods with Density-Ratio Correction. - Jiawei Huang; Nan Jiang 008 (AISTATS, 2022)

Leveraging Synergies Between AI and Networking to Build Next Generation Edge Networks. - Sen Lin 001; Ming Shi 003; Anish Arora; Raef Bassily; Elisa Bertino; Constantine Caramanis; Kaushik R. Chowdhury; Eylem Ekici; Atilla Eryilmaz; Stratis Ioannidis; Nan Jiang 008; Gauri Joshi; et al. (CIC, 2022)

Offline Reinforcement Learning with Realizability and Single-policy Concentrability. - Wenhao Zhan; Baihe Huang; Audrey Huang; Nan Jiang 008; Jason D. Lee (COLT, 2022)

Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality. - Jiawei Huang; Jinglin Chen; Li Zhao 007; Tao Qin 001; Nan Jiang 008; Tie-Yan Liu (ICLR, 2022)

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

A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes. - Chengchun Shi; Masatoshi Uehara; Jiawei Huang; Nan Jiang 008 (ICML, 2022)

A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation. - Philip Amortila; Nan Jiang 008; Dhruv Madeka; Dean P. Foster (NeurIPS, 2022)

On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL. - Jinglin Chen; Aditya Modi 002; Akshay Krishnamurthy; Nan Jiang 008; Alekh Agarwal (NeurIPS, 2022)

Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions. - Audrey Huang; Nan Jiang 008 (NeurIPS, 2022)

Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret. - Jiawei Huang; Li Zhao 007; Tao Qin 001; Wei Chen 034; Nan Jiang 008; Tie-Yan Liu (NeurIPS, 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)

Offline reinforcement learning under value and density-ratio realizability: The power of gaps. - Jinglin Chen; Nan Jiang 008 (UAI, 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)

Extended Abstract: Learning in Low-rank MDPs with Density Features. - Audrey Huang; Jinglin Chen; Nan Jiang 008 (CISS, 2023)

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

The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation. - Philip Amortila; Nan Jiang 008; Csaba Szepesvári (ICML, 2023)

Reinforcement Learning in Low-rank MDPs with Density Features. - Audrey Huang; Jinglin Chen; Nan Jiang 008 (ICML, 2023)

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

Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. - Masatoshi Uehara; Haruka Kiyohara; Andrew Bennett; Victor Chernozhukov; Nan Jiang 008; Nathan Kallus; Chengchun Shi; Wen Sun 002 (NeurIPS, 2023)

LM-Switch: Lightweight Language Model Conditioning in Word Embedding Space. - Chi Han; Jialiang Xu; Manling Li; Yi Fung 001; Chenkai Sun; Nan Jiang 008; Tarek F. Abdelzaher; Heng Ji 001 (CoRR, 2023)

Mitigating the Alignment Tax of RLHF. - Yong Lin; Hangyu Lin; Wei Xiong 015; Shizhe Diao; Jianmeng Liu; Jipeng Zhang; Rui Pan 002; Haoxiang Wang 003; Wenbin Hu 002; Hanning Zhang; Hanze Dong; Renjie Pi; et al. (CoRR, 2023)

Gibbs Sampling from Human Feedback: A Provable KL- constrained Framework for RLHF. - Wei Xiong 015; Hanze Dong; Chenlu Ye; Han Zhong 001; Nan Jiang 008; Tong Zhang 001 (CoRR, 2023)

Word Embeddings Are Steers for Language Models. - Chi Han; Jialiang Xu; Manling Li; Yi Fung 001; Chenkai Sun; Nan Jiang 008; Tarek F. Abdelzaher; Heng Ji 001 (ACL (1), 2024)

Mitigating the Alignment Tax of RLHF. - Yong Lin; Hangyu Lin; Wei Xiong 015; Shizhe Diao; Jianmeng Liu; Jipeng Zhang; Rui Pan 002; Haoxiang Wang 003; Wenbin Hu 002; Hanning Zhang; Hanze Dong; Renjie Pi; et al. (EMNLP, 2024)

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

Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint. - Wei Xiong 015; Hanze Dong; Chenlu Ye; Ziqi Wang 003; Han Zhong 001; Heng Ji 001; Nan Jiang 008; Tong Zhang 001 (ICML, 2024)

Reinforcement Learning Under Latent Dynamics: Toward Statistical and Algorithmic Modularity. - Philip Amortila; Dylan J. Foster; Nan Jiang 008; Akshay Krishnamurthy; Zakaria Mhammedi (NeurIPS, 2024)

Occupancy-based Policy Gradient: Estimation, Convergence, and Optimality. - Audrey Huang; Nan Jiang 008 (NeurIPS, 2024)

Online Iterative Reinforcement Learning from Human Feedback with General Preference Model. - Chenlu Ye; Wei Xiong 015; Yuheng Zhang; Hanze Dong; Nan Jiang 008; Tong Zhang 001 (NeurIPS, 2024)

Non-adaptive Online Finetuning for Offline Reinforcement Learning. - Audrey Huang; Mohammad Ghavamzadeh; Nan Jiang 008; Marek Petrik (RLJ, 2024)

A Theoretical Analysis of Nash Learning from Human Feedback under General KL-Regularized Preference. - Chenlu Ye; Wei Xiong 015; Yuheng Zhang; Nan Jiang 008; Tong Zhang 001 (CoRR, 2024)

Model-Free Representation Learning and Exploration in Low-Rank MDPs. - Aditya Modi 002; Jinglin Chen; Akshay Krishnamurthy; Nan Jiang 008; Alekh Agarwal (J. Mach. Learn. Res., 2024)

RLHF Workflow: From Reward Modeling to Online RLHF. - Hanze Dong; Wei Xiong 015; Bo Pang 004; Haoxiang Wang 003; Han Zhao 002; Yingbo Zhou; Nan Jiang 008; Doyen Sahoo; Caiming Xiong; Tong Zhang 001 (Trans. Mach. Learn. Res., 2024)

Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning. - Yuheng Zhang; Dian Yu 001; Baolin Peng; Linfeng Song; Ye Tian; Mingyue Huo; Nan Jiang 008; Haitao Mi; Dong Yu 001 (ICLR, 2025)

Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol. - Pai Liu; Lingfeng Zhao; Shivangi Agarwal; Jinghan Liu; Audrey Huang; Philip Amortila; Nan Jiang 008 (CoRR, 2025)

Improving LLM General Preference Alignment via Optimistic Online Mirror Descent. - Yuheng Zhang; Dian Yu 001; Tao Ge 005; Linfeng Song; Zhichen Zeng 001; Haitao Mi; Nan Jiang 008; Dong Yu 001 (CoRR, 2025)

Self-rewarding correction for mathematical reasoning. - Wei Xiong 015; Hanning Zhang; Chenlu Ye; Lichang Chen; Nan Jiang 008; Tong Zhang 001 (CoRR, 2025)

Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment. - Audrey Huang; Adam Block; Qinghua Liu; Nan Jiang 008; Dylan J. Foster; Akshay Krishnamurthy (CoRR, 2025)

A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce. - Wei Xiong 015; Jiarui Yao; Yuhui Xu; Bo Pang 004; Lei Wang 185; Doyen Sahoo; Junnan Li 001; Nan Jiang 008; Tong Zhang 001; Caiming Xiong; Hanze Dong (CoRR, 2025)

Optimizing Chain-of-Thought Reasoners via Gradient Variance Minimization in Rejection Sampling and RL. - Jiarui Yao; Yifan Hao 002; Hanning Zhang; Hanze Dong; Wei Xiong 015; Nan Jiang 008; Tong Zhang 001 (CoRR, 2025)

Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction. - Junhong Shen; Hao Bai; Lunjun Zhang; Yifei Zhou; Amrith Setlur; Shengbang Tong; Diego Caples; Nan Jiang 008; Tong Zhang 001; Ameet Talwalkar; Aviral Kumar (CoRR, 2025)