Nan Jiang

According to our database, Nan Jiang authored at least 65 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Model-Free Representation Learning and Exploration in Low-Rank MDPs.

Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal

J. Mach. Learn. Res., 2024

Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning.

Yuheng Zhang, Dian Yu, Baolin Peng, Linfeng Song, Ye Tian, Mingyue Huo, Nan Jiang, Haitao Mi, Dong Yu

CoRR, 2024

RLHF Workflow: From Reward Modeling to Online RLHF.

Hanze Dong, Wei Xiong, Bo Pang, Haoxiang Wang, Han Zhao, Yingbo Zhou, Nan Jiang, Doyen Sahoo, Caiming Xiong, Tong Zhang

CoRR, 2024

A Theoretical Analysis of Nash Learning from Human Feedback under General KL-Regularized Preference.

Chenlu Ye, Wei Xiong, Yuheng Zhang, Nan Jiang, Tong Zhang

CoRR, 2024

Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint.

Wei Xiong, Hanze Dong, Chenlu Ye, Ziqi Wang, Han Zhong, Heng Ji, Nan Jiang, Tong Zhang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Harnessing Density Ratios for Online Reinforcement Learning.

Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Mitigating the Alignment Tax of RLHF.

Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Heng Ji, Yuan Yao, Tong Zhang

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2023

Gibbs Sampling from Human Feedback: A Provable KL- constrained Framework for RLHF.

Wei Xiong, Hanze Dong, Chenlu Ye, Han Zhong, Nan Jiang, Tong Zhang

CoRR, 2023

Mitigating the Alignment Tax of RLHF.

Yong Lin, Hangyu Lin, Wei Xiong, Shizhe Diao, Jianmeng Liu, Jipeng Zhang, Rui Pan, Haoxiang Wang, Wenbin Hu, Hanning Zhang, Hanze Dong, Renjie Pi, Han Zhao, Nan Jiang, Yuan Yao, Tong Zhang

CoRR, 2023

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

Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang, Nathan Kallus, Chengchun Shi, Wen Sun

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Adversarial Model for Offline Reinforcement Learning.

Mohak Bhardwaj, Tengyang Xie, Byron Boots, Nan Jiang, Ching-An Cheng

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Reinforcement Learning in Low-rank MDPs with Density Features.

Audrey Huang, Jinglin Chen, Nan Jiang

Proceedings of the International Conference on Machine Learning, 2023

The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation.

Philip Amortila, Nan Jiang, Csaba Szepesvári

Proceedings of the International Conference on Machine Learning, 2023

The Role of Coverage in Online Reinforcement Learning.

Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang, Sham M. Kakade

Proceedings of the Eleventh International Conference on Learning Representations, 2023

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

Audrey Huang, Jinglin Chen, Nan Jiang

Proceedings of the 57th Annual Conference on Information Sciences and Systems, 2023

2022

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

Tengyang Xie, Mohak Bhardwaj, Nan Jiang, Ching-An Cheng

CoRR, 2022

Offline reinforcement learning under value and density-ratio realizability: The power of gaps.

Jinglin Chen, Nan Jiang

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Interaction-Grounded Learning with Action-Inclusive Feedback.

Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan P. Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

Jiawei Huang, Li Zhao, Tao Qin, Wei Chen, Nan Jiang, Tie-Yan Liu

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

Audrey Huang, Nan Jiang

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

Philip Amortila, Nan Jiang, Dhruv Madeka, Dean P. Foster

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

Chengchun Shi, Masatoshi Uehara, Jiawei Huang, Nan Jiang

Proceedings of the International Conference on Machine Learning, 2022

Adversarially Trained Actor Critic for Offline Reinforcement Learning.

Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal

Proceedings of the International Conference on Machine Learning, 2022

Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality.

Jiawei Huang, Jinglin Chen, Li Zhao, Tao Qin, Nan Jiang, Tie-Yan Liu

Proceedings of the Tenth International Conference on Learning Representations, 2022

Offline Reinforcement Learning with Realizability and Single-policy Concentrability.

Wenhao Zhan, Baihe Huang, Audrey Huang, Nan Jiang, Jason D. Lee

Proceedings of the Conference on Learning Theory, 2-5 July 2022, London, UK., 2022

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

Jiawei Huang, Nan Jiang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

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

Chengchun Shi, Masatoshi Uehara, Nan Jiang

CoRR, 2021

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

Masatoshi Uehara, Masaaki Imaizumi, Nan Jiang, Nathan Kallus, Wen Sun, Tengyang Xie

CoRR, 2021

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

Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, Yu Bai

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Bellman-consistent Pessimism for Offline Reinforcement Learning.

Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning.

Cameron Voloshin, Hoang Minh Le, Nan Jiang, Yisong Yue

Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks 1, 2021

Batch Value-function Approximation with Only Realizability.

Tengyang Xie, Nan Jiang

Proceedings of the 38th International Conference on Machine Learning, 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, Csaba Szepesvári

Proceedings of the Conference on Learning Theory, 2021

Minimax Model Learning.

Cameron Voloshin, Nan Jiang, Yisong Yue

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

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

Priyank Agrawal, Jinglin Chen, Nan Jiang

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

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

Philip Amortila, Nan Jiang, Tengyang Xie

CoRR, 2020

Q^* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison.

Tengyang Xie, Nan Jiang

CoRR, 2020

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

Nan Jiang, Jiawei Huang

CoRR, 2020

Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison.

Tengyang Xie, Nan Jiang

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

Minimax Value Interval for Off-Policy Evaluation and Policy Optimization.

Nan Jiang, Jiawei Huang

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Masatoshi Uehara, Jiawei Huang, Nan Jiang

Proceedings of the 37th International Conference on Machine Learning, 2020

From Importance Sampling to Doubly Robust Policy Gradient.

Jiawei Huang, Nan Jiang

Proceedings of the 37th International Conference on Machine Learning, 2020

Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles.

Aditya Modi, Nan Jiang, Ambuj Tewari, Satinder Singh

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

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

Masatoshi Uehara, Nan Jiang

CoRR, 2019

Provably Efficient Q-Learning with Low Switching Cost.

Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Provably efficient RL with Rich Observations via Latent State Decoding.

Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford

Proceedings of the 36th International Conference on Machine Learning, 2019

Information-Theoretic Considerations in Batch Reinforcement Learning.

Jinglin Chen, Nan Jiang

Proceedings of the 36th International Conference on Machine Learning, 2019

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

Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford

Proceedings of the Conference on Learning Theory, 2019

2018

Model-Based Reinforcement Learning in Contextual Decision Processes.

Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford

CoRR, 2018

On Polynomial Time PAC Reinforcement Learning with Rich Observations.

Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire

CoRR, 2018

Completing State Representations using Spectral Learning.

Nan Jiang, Alex Kulesza, Satinder Singh

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

On Oracle-Efficient PAC RL with Rich Observations.

Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Hierarchical Imitation and Reinforcement Learning.

Hoang Minh Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III

Proceedings of the 35th International Conference on Machine Learning, 2018

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

Nan Jiang, Alekh Agarwal

Proceedings of the Conference On Learning Theory, 2018

Markov Decision Processes with Continuous Side Information.

Aditya Modi, Nan Jiang, Satinder Singh, Ambuj Tewari

Proceedings of the Algorithmic Learning Theory, 2018

2017

Repeated Inverse Reinforcement Learning.

Kareem Amin, Nan Jiang, Satinder Singh

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

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

Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire

Proceedings of the 34th International Conference on Machine Learning, 2017

2016

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

Nan Jiang, Satinder Singh, Ambuj Tewari

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

The Dependence of Effective Planning Horizon on Model Accuracy.

Nan Jiang, Alex Kulesza, Satinder Singh, Richard L. Lewis

Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, 2016

Improving Predictive State Representations via Gradient Descent.

Nan Jiang, Alex Kulesza, Satinder Singh

Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 2016

2015

Abstraction Selection in Model-based Reinforcement Learning.

Nan Jiang, Alex Kulesza, Satinder Singh

Proceedings of the 32nd International Conference on Machine Learning, 2015

Low-Rank Spectral Learning with Weighted Loss Functions.

Alex Kulesza, Nan Jiang, Satinder Singh

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

Spectral Learning of Predictive State Representations with Insufficient Statistics.

Alex Kulesza, Nan Jiang, Satinder Singh

Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015

2014

Improving UCT planning via approximate homomorphisms.

Nan Jiang, Satinder Singh, Richard L. Lewis

Proceedings of the International conference on Autonomous Agents and Multi-Agent Systems, 2014