Nan Jiang

发表

Low-Rank Spectral Learning with Weighted Loss Functions

Nan Jiang, Alex Kulesza, Satinder P. Singh, 2015, AISTATS.

Improving UCT planning via approximate homomorphisms

Nan Jiang, Richard L. Lewis, Satinder P. Singh, 2014, AAMAS.

Markov Decision Processes with Continuous Side Information

Nan Jiang, Ambuj Tewari, Satinder P. Singh, 2017, ALT.

Doubly Robust Off-policy Value Evaluation for Reinforcement Learning

Nan Jiang, Lihong Li, Lihong Li, 2015, ICML.

Repeated Inverse Reinforcement Learning pdf

Nan Jiang, Kareem Amin, Satinder P. Singh, 2017, NIPS.

Hierarchical Imitation and Reinforcement Learning pdf

Nan Jiang, Yisong Yue, Miroslav Dudík, 2018, ICML.

From Importance Sampling to Doubly Robust Policy Gradient

Nan Jiang, Jiawei Huang, 2019, ICML.

Reinforcement Learning: Theory and Algorithms

S. Kakade, Alekh Agarwal, Nan Jiang, 2019 .

M ar 2 02 0 Q ⋆ Approximation Schemes for Batch Reinforcement Learning : A eoretical Comparison

Nan Jiang, Tengyang Xie, 2020 .

L G ] 1 M ay 2 01 9 Information-Theoretic Considerations in Batch Reinforcement Learning

Nan Jiang, Jinglin Chen, 2019 .

Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning

Hoang Minh Le, Yisong Yue, Nan Jiang, 2019, NeurIPS Datasets and Benchmarks.

Nan Jiang- Research Statement

Nan Jiang, 2016 .

Abstraction Selection in Model-based Reinforcement Learning

Nan Jiang, Alex Kulesza, Satinder P. Singh, 2015, ICML.

The Caltech Off-Policy Policy Evaluation Benchmarking Suite

Nan Jiang, Cameron Voloshin, 2021 .

Tabular Methods

Nan Jiang, 2018 .

Notes on Tabular Methods

Nan Jiang, 2018 .

Minimax Confidence Interval for Off-Policy Evaluation and Policy Optimization pdf

Jiawei Huang, Nan Jiang, Nan Jiang, 2020, ArXiv.

Notes on State Abstractions

Nan Jiang, 2018 .

Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality

Tie-Yan Liu, Nan Jiang, Tao Qin, 2022, ArXiv.

Doubly Robust Off-policy Evaluation for Reinforcement Learning pdf

Nan Jiang, Lihong Li, Lihong Li, 2015, ArXiv.

Bellman-consistent Pessimism for Offline Reinforcement Learning

Alekh Agarwal, Nan Jiang, Paul Mineiro, 2021, Neural Information Processing Systems.

Offline Reinforcement Learning with Realizability and Single-policy Concentrability

Jason D. Lee, Nan Jiang, Audrey Huang, 2022, COLT.

Interaction-Grounded Learning with Action-inclusive Feedback

Dylan J. Foster, J. Langford, I. Momennejad, 2022, ArXiv.

Adversarially Trained Actor Critic for Offline Reinforcement Learning

Alekh Agarwal, Nan Jiang, Tengyang Xie, 2022, ICML.

Future-Dependent Value-Based Off-Policy Evaluation in POMDPs

V. Chernozhukov, Masatoshi Uehara, Nan Jiang, 2022, ArXiv.

A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes

Masatoshi Uehara, Nan Jiang, Jiawei Huang, 2021, ICML.

Interaction-Grounded Learning with Action-inclusive Feedback

Dylan J. Foster, J. Langford, I. Momennejad, 2022, NeurIPS.

Trajectory-based Explainability Framework for Offline RL

Georgios Theocharous, Nan Jiang, Chirag Agarwal, 2022 .

Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency

Masatoshi Uehara, Nan Jiang, Wen Sun, 2021, ArXiv.

Learning Combinatorial Structures via Markov Random Fields with Sampling through Lovász Local Lemma

Nan Jiang, Yexiang Xue, Yi Gu, 2022, ArXiv.

ARMOR: A Model-based Framework for Improving Arbitrary Baseline Policies with Offline Data

Nan Jiang, Tengyang Xie, Ching-An Cheng, 2022, ArXiv.

Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret

Tie-Yan Liu, Nan Jiang, Tao Qin, 2022, ArXiv.

Model-free Representation Learning and Exploration in Low-rank MDPs

A. Krishnamurthy, Alekh Agarwal, Nan Jiang, 2021, ArXiv.

On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL

A. Krishnamurthy, Alekh Agarwal, Nan Jiang, 2022, NeurIPS.

The Role of Coverage in Online Reinforcement Learning

Dylan J. Foster, S. Kakade, Nan Jiang, 2022, ICLR.

A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation

Dean Phillips Foster, Nan Jiang, P. Amortila, 2022, NeurIPS.

Offline Reinforcement Learning Under Value and Density-Ratio Realizability: the Power of Gaps

Nan Jiang, Jinglin Chen, 2022, UAI.

Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality

Tie-Yan Liu, Nan Jiang, Tao Qin, 2022, ICLR.

Offline Learning in Markov Games with General Function Approximation

Nan Jiang, Yunru Bai, Yuheng Zhang, 2023, ICML.

Contextual Decision Processes with low Bellman rank are PAC-Learnable pdf

Nan Jiang, John Langford, Akshay Krishnamurthy, 2016, ICML.

Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches

Nan Jiang, John Langford, Akshay Krishnamurthy, 2018, COLT.

On Oracle-Efficient PAC RL with Rich Observations

Nan Jiang, John Langford, Akshay Krishnamurthy, 2018, NeurIPS.

RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement Learning pdf

Nan Jiang, Sheng Jin, Zhiyao Duan, 2020, AAAI.

Provably Efficient Q-Learning with Low Switching Cost

Yu Bai, Nan Jiang, Yu-Xiang Wang, 2019, NeurIPS.

PAC Reinforcement Learning With an Imperfect Model

Nan Jiang, Nan Jiang, 2018, AAAI.

Binary Q-matrix Learning with dAFM

Nan Jiang, Zachary A. Pardos, Z. Pardos, 2019, Educational Data Mining.

Hierarchical automatic curriculum learning: Converting a sparse reward navigation task into dense reward

Nan Jiang, Sheng Jin, Changshui Zhang, 2019, Neurocomputing.

Provably efficient RL with Rich Observations via Latent State Decoding pdf

Nan Jiang, John Langford, Akshay Krishnamurthy, 2019, ICML.

Information-Theoretic Considerations in Batch Reinforcement Learning pdf

Nan Jiang, Jinglin Chen, Nan Jiang, 2019, ICML.

Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles

Ambuj Tewari, Satinder Singh, Nan Jiang, 2019, AISTATS.

Minimax Weight and Q-Function Learning for Off-Policy Evaluation

Masatoshi Uehara, Nan Jiang, Masatoshi Uehara, 2019, ICML.

Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning

Caiming Xiong, Nan Jiang, Tengyang Xie, 2021, NeurIPS.

Batch Value-function Approximation with Only Realizability

Nan Jiang, Tengyang Xie, Nan Jiang, 2020, ICML.

Minimax Value Interval for Off-Policy Evaluation and Policy Optimization

Nan Jiang, Jiawei Huang, 2020, NeurIPS.

$Q^\star$ Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison pdf

Nan Jiang, Tengyang Xie, Nan Jiang, 2020, 2003.03924.

A Minimax Learning Approach to Off-Policy Evaluation in Partially Observable Markov Decision Processes pdf

Masatoshi Uehara, Chengchun Shi, Nan Jiang, 2021, ArXiv.

The Dependence of Effective Planning Horizon on Model Accuracy

Nan Jiang, Alex Kulesza, Richard L. Lewis, 2015, AAMAS.

On Structural Properties of MDPs that Bound Loss Due to Shallow Planning

Nan Jiang, Ambuj Tewari, Satinder P. Singh, 2016, IJCAI.

ALSO-X#: Better Convex Approximations for Distributionally Robust Chance Constrained Programs

Nan Jiang, Weijun Xie, 2023, 2302.01737.

ALSO-X and ALSO-X+: Better Convex Approximations for Chance Constrained Programs

Nan Jiang, Weijun Xie, Nan Jiang, 2020, Oper. Res..

On Value Functions and the Agent-Environment Boundary pdf

Nan Jiang, Nan Jiang, 2019, ArXiv.

Open Problem: The Dependence of Sample Complexity Lower Bounds on Planning Horizon

Nan Jiang, Alekh Agarwal, Alekh Agarwal, 2018, COLT.

Notes on Importance Sampling and Policy Gradient

Nan Jiang, 2018 .

Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning pdf

Nan Jiang, Siyuan Zhang, Nan Jiang, 2021, NeurIPS.

Extended Abstract: Learning in Low-rank MDPs with Density Features

Nan Jiang, Jinglin Chen, Audrey Huang, 2023, 2023 57th Annual Conference on Information Sciences and Systems (CISS).

Reinforcement Learning in Low-Rank MDPs with Density Features

Nan Jiang, Jinglin Chen, Audrey Huang, 2023, ICML.

On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function

Csaba Szepesvari, G. Weisz, Yasin Abbasi-Yadkori, 2021, COLT.

Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration

Nan Jiang, Jinglin Chen, Priyank Agrawal, 2020, AAAI.

Notes on Exploration in Linear MDPs

Nan Jiang, Wen Sun, 2022 .

Adversarial Model for Offline Reinforcement Learning

Byron Boots, Nan Jiang, Tengyang Xie, 2023, NeurIPS.

Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions

Nan Jiang, Audrey Huang, 2022, NeurIPS.

A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting pdf

Philip Amortila, Tengyang Xie, Nan Jiang, 2020, ArXiv.

Lecture Notes on the Theory of Reinforcement Learning

S. Kakade, Alekh Agarwal, Nan Jiang, 2019 .

Lectures on the Theory of Reinforcement Learning

S. Kakade, Alekh Agarwal, Nan Jiang, 2019 .

Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches

J. Langford, A. Krishnamurthy, Alekh Agarwal, 2018, COLT.

Completing State Representations using Spectral Learning

Nan Jiang, Alex Kulesza, Satinder P. Singh, 2018, NeurIPS.

When Counterpoint Meets Chinese Folk Melodies

Zhiyao Duan, Changshui Zhang, Sheng Jin, 2020, NeurIPS.

Explaining RL Decisions with Trajectories

Georgios Theocharous, Nan Jiang, Balaji Krishnamurthy, 2023, ICLR.

Doubly Robust Off-policy Evaluation for Reinforcement Learning

Lihong Li, Nan Jiang, 2015, ArXiv.

On Oracle-Efficient PAC Reinforcement Learning with Rich Observations

Nan Jiang, John Langford, Akshay Krishnamurthy, 2018 .

On Polynomial Time PAC Reinforcement Learning with Rich Observations pdf

Nan Jiang, John Langford, Akshay Krishnamurthy, 2018, ArXiv.

Minimax Model Learning pdf

Nan Jiang, Cameron Voloshin, Yisong Yue, 2021, AISTATS.

A Theory of Model Selection in Reinforcement Learning

Nan Jiang, Nan Jiang, 2017 .

Neural Network Architecture Optimization through Submodularity and Supermodularity pdf

Changshui Zhang, Nan Jiang, Junqi Jin, 2016, ArXiv.

Optimizing Recurrent Neural Networks Architectures under Time Constraints pdf

Changshui Zhang, Nan Jiang, Junqi Jin, 2016, ArXiv.

On the Convergence Rate of Off-Policy Policy Optimization Methods with Density-Ratio Correction

Nan Jiang, Jiawei Huang, 2021, AISTATS.

Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret

Tie-Yan Liu, Nan Jiang, Tao Qin, 2022, NeurIPS.

ALSO-X is Better Than CVaR: Convex Approximations for Chance Constrained Programs Revisited

Nan Jiang, Weijun Xie, 2020 .

Future-Dependent Value-Based Off-Policy Evaluation in POMDPs

V. Chernozhukov, Masatoshi Uehara, Nan Jiang, 2022, NeurIPS.

Bellman-consistent Pessimism for Offline Reinforcement Learning

Alekh Agarwal, Nan Jiang, Paul Mineiro, 2021, NeurIPS.