Google Scholar

Reinforcement Learning: Theory and Algorithms [working draft]
(Monograph) Alekh Agarwal, Nan Jiang, Sham Kakade, Wen Sun. (remark)

(* = Equal contribution or alphabetical)

On the Curses of Future and History in Future-dependent Value Functions for Off-policy Evaluation [arXiv, slides]
(preprint) Yuheng Zhang, Nan Jiang.

A Theoretical Analysis of Nash Learning from Human Feedback under General KL-Regularized Preference [arXiv]
(preprint) Chenlu Ye*, Wei Xiong*, Yuheng Zhang*, Nan Jiang, Tong Zhang.

Non-adaptive Online Finetuning for Offline Reinforcement Learning [openreview]
(RLC-24) Audrey Huang, Mohammad Ghavamzadeh, Nan Jiang, Marek Petrik.

Word Embeddings Are Steers for Language Models [openreview]
(ACL-24) Chi Han, Jialiang Xu, Manling Li, Yi Fung, Chenkai Sun, Nan Jiang, Tarek F. Abdelzaher, Heng Ji.

Gibbs Sampling from Human Feedback: A Provable KL-constrained Framework for RLHF [arXiv]
(ICML-24) Wei Xiong*, Hanze Dong*, Chenlu Ye*, Ziqi Wang, Han Zhong, Heng Ji, Nan Jiang, Tong Zhang.

Harnessing Density Ratios for Online Reinforcement Learning [arXiv]
(ICLR-24, spotlight) Philip Amortila*, Dylan Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie.

Model-free Representation Learning and Exploration in Low-rank MDPs [pdf]
(JMLR-24) Aditya Modi*, Jinglin Chen*, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal.

Future-Dependent Value-Based Off-Policy Evaluation in POMDPs [arXiv]
(NeurIPS-23, spotlight) Masatoshi Uehara*, Haruka Kiyohara*, Andrew Bennett, Victor Chernozhukov, Nan Jiang, Nathan Kallus, Chengchun Shi, Wen Sun.

Adversarial Model for Offline Reinforcement Learning [arXiv]
(NeurIPS-23) Mohak Bhardwaj*, Tengyang Xie*, Byron Boots, Nan Jiang, Ching-An Cheng.

Marginalized Importance Sampling for Off-Environment Policy Evaluation [arXiv]
(CoRL-23) Pulkit Katdare, Nan Jiang, Katherine Driggs-Campbell.

Reinforcement Learning in Low-Rank MDPs with Density Features [arXiv]
(ICML-23) Audrey Huang*, Jinglin Chen*, Nan Jiang.

The Optimal Approximation Ratios in Misspecified Off-Policy Value Function Estimation [arXiv (stronger results than conf ver.)]
(ICML-23) Philip Amortila, Nan Jiang, Csaba Szepesvari.

Offline Learning in Markov Games with General Function Approximation [arXiv]
(ICML-23) Yuheng Zhang, Yu Bai, Nan Jiang.

The Role of Coverage in Online Reinforcement Learning [arXiv]
(ICLR-23, notable top-5% ) Tengyang Xie*, Dylan J Foster*, Yu Bai, Nan Jiang, Sham Kakade.

Explaining RL Decisions with Trajectories [openreview]
(ICLR-23) Shripad Vilasrao Deshmukh, Arpan Dasgupta, Balaji Krishnamurthy, Nan Jiang, Chirag Agarwal, Georgios Theocharous, Jayakumar Subramanian.

Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions [arXiv]
(NeurIPS-22) Audrey Huang, Nan Jiang.

A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation [arXiv]
(NeurIPS-22) Philip Amortila, Nan Jiang, Dhruv Madeka, Dean P. Foster.

On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL [arXiv]
(NeurIPS-22) Jinglin Chen*, Aditya Modi*, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal.

Interaction-Grounded Learning with Action-inclusive Feedback [arXiv]
(NeurIPS-22) Tengyang Xie*, Akanksha Saran*, Dylan J Foster, Lekan Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford.

Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret [arXiv]
(NeurIPS-22) Jiawei Huang, Li Zhao, Tao Qin, Wei Chen, Nan Jiang, Tie-Yan Liu.

Offline Reinforcement Learning Under Value and Density-Ratio Realizability: the Power of Gaps [arXiv]
(UAI-22) Jinglin Chen, Nan Jiang.

Offline Reinforcement Learning with Realizability and Single-policy Concentrability [arXiv]
(COLT-22) Wenhao Zhan, Baihe Huang, Audrey Huang, Nan Jiang, Jason D. Lee.

Adversarially Trained Actor Critic for Offline Reinforcement Learning [arXiv]
(ICML-22, Outstanding Paper Runner-up ) Ching-An Cheng*, Tengyang Xie*, Nan Jiang, Alekh Agarwal.

A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes [arXiv]
(ICML-22) Chengchun Shi*, Masatoshi Uehara*, Jiawei Huang, Nan Jiang.

Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality [arXiv]
(ICLR-22) Jiawei Huang, Jinglin Chen, Li Zhao, Tao Qin, Nan Jiang, Tie-Yan Liu.

On the Convergence Rate of Off-Policy Policy Optimization Methods with Density-Ratio Correction [arXiv]
(AISTATS-22) Jiawei Huang, Nan Jiang.

Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning [arXiv, code]
(NeurIPS-21) Siyuan Zhang, Nan Jiang.

Bellman-consistent Pessimism for Offline Reinforcement Learning [arXiv]
(NeurIPS-21, oral presentation) Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal.

Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning [arXiv]
(NeurIPS-21) Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, Yu Bai.

Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning [arXiv]
(NeurIPS-21 Datasets and Benchmarks) Cameron Voloshin, Hoang M. Le, Nan Jiang, Yisong Yue.

On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function [arXiv]
(COLT-21) Gellert Weisz, Philip Amortila, Barnabás Janzer, Yasin Abbasi-Yadkori, Nan Jiang, Csaba Szepesvári.

Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency [arXiv]
(preprint) Masatoshi Uehara, Masaaki Imaizumi, Nan Jiang, Nathan Kallus, Wen Sun, Tengyang Xie.

A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting [arXiv]
(Technical Note) Philip Amortila*, Nan Jiang, Tengyang Xie.

Batch Value-function Approximation with Only Realizability [arXiv, talk]
(ICML-21) Tengyang Xie, Nan Jiang.

Minimax Model Learning [arXiv]
(AISTATS-21) Cameron Voloshin, Nan Jiang, Yisong Yue.

Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration [arXiv]
(AAAI-21) Priyank Agrawal*, Jinglin Chen*, Nan Jiang.

Minimax Value Interval for Off-Policy Evaluation and Policy Optimization [arXiv]
(NeurIPS-20) Nan Jiang, Jiawei Huang.

Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison [arXiv]
(UAI-20) Tengyang Xie, Nan Jiang.

Minimax Weight and Q-Function Learning for Off-Policy Evaluation [arXiv]
(ICML-20) Masatoshi Uehara, Jiawei Huang, Nan Jiang.

From Importance Sampling to Doubly Robust Policy Gradient [arXiv]
(ICML-20) Jiawei Huang, Nan Jiang.

On Value Functions and the Agent-Environment Boundary [arXiv]
(Technical Note) Nan Jiang.

Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles [arXiv]
(AISTATS-20) Aditya Modi, Nan Jiang, Ambuj Tewari, Satinder Singh.

Provably Efficient Q-Learning with Low Switching Cost [arXiv]
(NeurIPS-19) Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang.

Deterministic Bellman Residual Minimization [pdf]
(OptRL Workshop at NeurIPS-19) Ehsan Saleh, Nan Jiang.

Information-Theoretic Considerations in Batch Reinforcement Learning [pdf, poster, MSR talk, Simons talk]
(ICML-19) Jinglin Chen, Nan Jiang.

Provably Efficient RL with Rich Observations via Latent State Decoding [arXiv]
(ICML-19) Simon Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford.

Model-based RL in CDPs: PAC bounds and Exponential Improvements over Model-free Approaches [arXiv]
(COLT-19) Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford.

On Oracle-Efficient PAC RL with Rich Observations [arXiv]
(NeurIPS-18, spotlight talk) Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire.

Completing State Representations using Spectral Learning [pdf, code, poster]
(NeurIPS-18) Nan Jiang, Alex Kulesza, Satinder Singh.

Open Problem: The Dependence of Sample Complexity Lower Bounds on Planning Horizon [pdf]
(COLT-18) Nan Jiang, Alekh Agarwal.

Hierarchical Imitation and Reinforcement Learning [arXiv]
(ICML-18) Hoang M. Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III.

Markov Decision Processes with Continuous Side Information [arXiv]
(ALT-18) Aditya Modi, Nan Jiang, Satinder Singh, Ambuj Tewari.

PAC Reinforcement Learning with an Imperfect Model [pdf, poster]
(AAAI-18) Nan Jiang.

Repeated Inverse Reinforcement Learning [arXiv, errata, poster, talk video]
(NeurIPS-17, spotlight talk) Kareem Amin*, Nan Jiang*, Satinder Singh.

Contextual Decision Processes with Low Bellman Rank are PAC-Learnable [ICML version, arXiv, errata, poster, talk video]
(ICML-17) Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire.

Doubly Robust Off-policy Value Evaluation for Reinforcement Learning [pdf, poster]
(ICML-16) Nan Jiang, Lihong Li.

On Structural Properties of MDPs that Bound Loss due to Shallow Planning [pdf]
(IJCAI-16) Nan Jiang, Satinder Singh, Ambuj Tewari.

Improving Predictive State Representations via Gradient Descent [pdf, poster]
(AAAI-16) Nan Jiang, Alex Kulesza, Satinder Singh.

Abstraction Selection in Model-based Reinforcement Learning [pdf, talk video]
(ICML-15) Nan Jiang, Alex Kulesza, Satinder Singh.

The Dependence of Effective Planning Horizon on Model Accuracy [pdf, errata, poster, talk video]
(AAMAS-15, Best Paper Award) Nan Jiang, Alex Kulesza, Satinder Singh, Richard Lewis.

Low-Rank Spectral Learning with Weighted Loss Functions [pdf]
(AISTATS-15) Alex Kulesza, Nan Jiang, Satinder Singh.

Spectral Learning of Predictive State Representations with Insufficient Statistics [pdf]
(AAAI-15) Alex Kulesza, Nan Jiang, Satinder Singh.

Improving UCT Planning via Approximate Homomorphisms [pdf, supplement]
(AAMAS-14) Nan Jiang, Satinder Singh, Richard Lewis.

PhD Thesis

A Theory of Model Selection in Reinforcement Learning. [pdf]
(2017) Nan Jiang.