Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning [arXiv, code]
(NeurIPS-21) Siyuan Zhang, Nan Jiang.
BVFT shows promising empirical performance for offline policy selection.

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.
Cute tensorization trick for generative model + linear V*.

Batch Value-function Approximation with Only Realizability [arXiv, talk]
(ICML-21) Tengyang Xie, Nan Jiang.
Learning Q* from a realizable and otherwise arbitrary function class, which was believed to be impossible

Minimax Value Interval for Off-Policy Evaluation and Policy Optimization [arXiv]
(NeurIPS-20) Nan Jiang, Jiawei Huang.
Unifying "weight-learning" and "value-learning" methods for MIS-based OPE under an interval that quantifies the biases due to function approximation.

Minimax Weight and Q-Function Learning for Off-Policy Evaluation [arXiv]
(ICML-20) Masatoshi Uehara, Jiawei Huang, Nan Jiang.
Learning importance weights and value functions from each other, with connections to many old and new algorithms in RL.

Information-Theoretic Considerations in Batch Reinforcement Learning [pdf, poster, MSR talk, Simons talk]
(ICML-19) Jinglin Chen, Nan Jiang.
Revisiting some fundamental aspects of value-based RL.

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.
A new and general theory of exploration in RL with function approximation.

Doubly Robust Off-policy Value Evaluation for Reinforcement Learning [pdf, poster]
(ICML-16) Nan Jiang, Lihong Li.
Simple and effective improvement of importance sampling via control variates.

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.
Using a smaller discount factor than defined can be viewed as regularization.

Low-Rank Spectral Learning with Weighted Loss Functions [pdf]
(AISTATS-15) Alex Kulesza, Nan Jiang, Satinder Singh.
Approximation guarantees for low-rank learning of PSRs.