Nan Jiang

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 45

Top Topics

Markov Decision Process

Reinforcement Learning

Predictive State Representations

Partially Observable

Top Venues

Publications

Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie
Harnessing Density Ratios for Online Reinforcement Learning. CoRR (2024)
Yuheng Zhang, Dian Yu, Baolin Peng, Linfeng Song, Ye Tian, Mingyue Huo, Nan Jiang, Haitao Mi, Dong Yu
Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning. CoRR (2024)
Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie
Harnessing Density Ratios for Online Reinforcement Learning. ICLR (2024)
Hanze Dong, Wei Xiong, Bo Pang, Haoxiang Wang, Han Zhao, Yingbo Zhou, Nan Jiang, Doyen Sahoo, Caiming Xiong, Tong Zhang
RLHF Workflow: From Reward Modeling to Online RLHF. CoRR (2024)
Audrey Huang, Jinglin Chen, Nan Jiang
Reinforcement Learning in Low-rank MDPs with Density Features. ICML (2023)
Mohak Bhardwaj, Tengyang Xie, Byron Boots, Nan Jiang, Ching-An Cheng
Adversarial Model for Offline Reinforcement Learning. CoRR (2023)
Philip Amortila, Nan Jiang, Csaba Szepesvári
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation. CoRR (2023)
Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang, Nathan Kallus, Chengchun Shi, Wen Sun
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. NeurIPS (2023)
Philip Amortila, Nan Jiang, Csaba Szepesvári
The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation. ICML (2023)
Mohak Bhardwaj, Tengyang Xie, Byron Boots, Nan Jiang, Ching-An Cheng
Adversarial Model for Offline Reinforcement Learning. NeurIPS (2023)
Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang, Sham M. Kakade
The Role of Coverage in Online Reinforcement Learning. ICLR (2023)
Jinglin Chen, Nan Jiang
Offline Reinforcement Learning Under Value and Density-Ratio Realizability: the Power of Gaps. CoRR (2022)
Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL. NeurIPS (2022)
Jinglin Chen, Nan Jiang
Offline reinforcement learning under value and density-ratio realizability: The power of gaps. UAI (2022)
Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang, Sham M. Kakade
The Role of Coverage in Online Reinforcement Learning. CoRR (2022)
Philip Amortila, Nan Jiang, Dhruv Madeka, Dean P. Foster
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation. NeurIPS (2022)
Tengyang Xie, Mohak Bhardwaj, Nan Jiang, Ching-An Cheng
ARMOR: A Model-based Framework for Improving Arbitrary Baseline Policies with Offline Data. CoRR (2022)
Philip Amortila, Nan Jiang, Dhruv Madeka, Dean P. Foster
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation. CoRR (2022)
Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal
Adversarially Trained Actor Critic for Offline Reinforcement Learning. ICML (2022)
Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan P. Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford
Interaction-Grounded Learning with Action-Inclusive Feedback. NeurIPS (2022)
Audrey Huang, Nan Jiang
Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions. NeurIPS (2022)
Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL. CoRR (2022)
Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang, Nathan Kallus, Chengchun Shi, Wen Sun
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. CoRR (2022)
Jiawei Huang, Li Zhao, Tao Qin, Wei Chen, Nan Jiang, Tie-Yan Liu
Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret. NeurIPS (2022)
Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford
Interaction-Grounded Learning with Action-inclusive Feedback. CoRR (2022)
Jiawei Huang, Jinglin Chen, Li Zhao, Tao Qin, Nan Jiang, Tie-Yan Liu
Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality. ICLR (2022)
Chengchun Shi, Masatoshi Uehara, Jiawei Huang, Nan Jiang
A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes. ICML (2022)
Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal
Model-free Representation Learning and Exploration in Low-rank MDPs. CoRR (2021)
Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal
Bellman-consistent Pessimism for Offline Reinforcement Learning. NeurIPS (2021)
Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, Yu Bai
Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning. NeurIPS (2021)
Cameron Voloshin, Hoang Minh Le, Nan Jiang, Yisong Yue
Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning. NeurIPS Datasets and Benchmarks (2021)
Aditya Modi, Nan Jiang, Ambuj Tewari, Satinder P. Singh
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles. AISTATS (2020)
Aditya Modi, Nan Jiang, Ambuj Tewari, Satinder P. Singh
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles. CoRR (2019)
Nan Jiang, Alex Kulesza, Satinder P. Singh
Completing State Representations using Spectral Learning. NeurIPS (2018)
Aditya Modi, Nan Jiang, Satinder P. Singh, Ambuj Tewari
Markov Decision Processes with Continuous Side Information. ALT (2018)
Kareem Amin, Nan Jiang, Satinder P. Singh
Repeated Inverse Reinforcement Learning. NIPS (2017)
Aditya Modi, Nan Jiang, Satinder P. Singh, Ambuj Tewari
Markov Decision Processes with Continuous Side Information. CoRR (2017)
Kareem Amin, Nan Jiang, Satinder P. Singh
Repeated Inverse Reinforcement Learning. CoRR (2017)
Nan Jiang, Satinder P. Singh, Ambuj Tewari
On Structural Properties of MDPs that Bound Loss Due to Shallow Planning. IJCAI (2016)
Nan Jiang, Alex Kulesza, Satinder P. Singh, Richard L. Lewis
The Dependence of Effective Planning Horizon on Model Accuracy. IJCAI (2016)
Nan Jiang, Alex Kulesza, Satinder P. Singh
Improving Predictive State Representations via Gradient Descent. AAAI (2016)
Alex Kulesza, Nan Jiang, Satinder P. Singh
Low-Rank Spectral Learning with Weighted Loss Functions. AISTATS (2015)
Alex Kulesza, Nan Jiang, Satinder P. Singh
Spectral Learning of Predictive State Representations with Insufficient Statistics. AAAI (2015)
Nan Jiang, Alex Kulesza, Satinder P. Singh
Abstraction Selection in Model-based Reinforcement Learning. ICML (2015)
Nan Jiang, Alex Kulesza, Satinder P. Singh, Richard L. Lewis
The Dependence of Effective Planning Horizon on Model Accuracy. AAMAS (2015)
Nan Jiang, Satinder P. Singh, Richard L. Lewis
Improving UCT planning via approximate homomorphisms. AAMAS (2014)