Nan Jiang - RankMe

120 papers A* 44A 8B 1Misc 1Journal 62Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	A Unifying View of Coverage in Linear Off-Policy Evaluation. CoRR Philip Amortila, Audrey Huang, Akshay Krishnamurthy, Nan Jiang
2025	J	`jnl`	A Minimalist Approach to LLM Reasoning: from Rejection Sampling to Reinforce. CoRR Wei Xiong, Jiarui Yao, Yuhui Xu, Bo Pang, Lei Wang, Doyen Sahoo, Junnan Li, Nan Jiang, Tong Zhang, Caiming Xiong, Hanze Dong
2025	J	`jnl`	A Snapshot of Influence: A Local Data Attribution Framework for Online Reinforcement Learning. CoRR Yuzheng Hu, Fan Wu, Haotian Ye, David A. Forsyth, James Zou, Nan Jiang, Jiaqi W. Ma, Han Zhao
2025	J	`jnl`	Improving LLM General Preference Alignment via Optimistic Online Mirror Descent. CoRR Yuheng Zhang, Dian Yu, Tao Ge, Linfeng Song, Zhichen Zeng, Haitao Mi, Nan Jiang, Dong Yu
2025	A*	`conf`	Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment. ICML Audrey Huang, Adam Block, Qinghua Liu, Nan Jiang, Akshay Krishnamurthy, Dylan J. Foster
2025	J	`jnl`	Is Best-of-N the Best of Them? Coverage, Scaling, and Optimality in Inference-Time Alignment. CoRR Audrey Huang, Adam Block, Qinghua Liu, Nan Jiang, Dylan J. Foster, Akshay Krishnamurthy
2025	A*	`conf`	Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning. ICLR Yuheng Zhang, Dian Yu, Baolin Peng, Linfeng Song, Ye Tian, Mingyue Huo, Nan Jiang, Haitao Mi, Dong Yu
2025	J	`jnl`	Model Selection for Off-policy Evaluation: New Algorithms and Experimental Protocol. CoRR Pai Liu, Lingfeng Zhao, Shivangi Agarwal, Jinghan Liu, Audrey Huang, Philip Amortila, Nan Jiang
2025	J	`jnl`	Optimizing Chain-of-Thought Reasoners via Gradient Variance Minimization in Rejection Sampling and RL. CoRR Jiarui Yao, Yifan Hao, Hanning Zhang, Hanze Dong, Wei Xiong, Nan Jiang, Tong Zhang
2025	J	`jnl`	Reinforce-Ada: An Adaptive Sampling Framework for Reinforce-Style LLM Training. CoRR Wei Xiong, Chenlu Ye, Baohao Liao, Hanze Dong, Xinxing Xu, Christof Monz, Jiang Bian, Nan Jiang, Tong Zhang
2025	J	`jnl`	Self-rewarding correction for mathematical reasoning. CoRR Wei Xiong, Hanning Zhang, Chenlu Ye, Lichang Chen, Nan Jiang, Tong Zhang
2025	J	`jnl`	Thinking vs. Doing: Agents that Reason by Scaling Test-Time Interaction. CoRR Junhong Shen, Hao Bai, Lunjun Zhang, Yifei Zhou, Amrith Setlur, Shengbang Tong, Diego Caples, Nan Jiang, Tong Zhang, Ameet Talwalkar, Aviral Kumar
2024	J	`jnl`	A Theoretical Analysis of Nash Learning from Human Feedback under General KL-Regularized Preference. CoRR Chenlu Ye, Wei Xiong, Yuheng Zhang, Nan Jiang, Tong Zhang
2024	A*	`conf`	Harnessing Density Ratios for Online Reinforcement Learning. ICLR Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie
2024	J	`jnl`	Harnessing Density Ratios for Online Reinforcement Learning. CoRR Philip Amortila, Dylan J. Foster, Nan Jiang, Ayush Sekhari, Tengyang Xie
2024	J	`jnl`	Iterative Nash Policy Optimization: Aligning LLMs with General Preferences via No-Regret Learning. CoRR Yuheng Zhang, Dian Yu, Baolin Peng, Linfeng Song, Ye Tian, Mingyue Huo, Nan Jiang, Haitao Mi, Dong Yu
2024	A*	`conf`	Iterative Preference Learning from Human Feedback: Bridging Theory and Practice for RLHF under KL-constraint. ICML Wei Xiong, Hanze Dong, Chenlu Ye, Ziqi Wang, Han Zhong, Heng Ji, Nan Jiang, Tong Zhang
2024	A*	`conf`	Mitigating the Alignment Tax of RLHF. EMNLP 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
2024	J	`jnl`	Model-Free Representation Learning and Exploration in Low-Rank MDPs. J. Mach. Learn. Res. Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal
2024	J	`jnl`	Non-adaptive Online Finetuning for Offline Reinforcement Learning. RLJ Audrey Huang, Mohammad Ghavamzadeh, Nan Jiang, Marek Petrik
2024	A*	`conf`	Occupancy-based Policy Gradient: Estimation, Convergence, and Optimality. NeurIPS Audrey Huang, Nan Jiang
2024	A*	`conf`	Online Iterative Reinforcement Learning from Human Feedback with General Preference Model. NeurIPS Chenlu Ye, Wei Xiong, Yuheng Zhang, Hanze Dong, Nan Jiang, Tong Zhang
2024	J	`jnl`	RLHF Workflow: From Reward Modeling to Online RLHF. CoRR Hanze Dong, Wei Xiong, Bo Pang, Haoxiang Wang, Han Zhao, Yingbo Zhou, Nan Jiang, Doyen Sahoo, Caiming Xiong, Tong Zhang
2024	J	`jnl`	RLHF Workflow: From Reward Modeling to Online RLHF. Trans. Mach. Learn. Res. Hanze Dong, Wei Xiong, Bo Pang, Haoxiang Wang, Han Zhao, Yingbo Zhou, Nan Jiang, Doyen Sahoo, Caiming Xiong, Tong Zhang
2024	A*	`conf`	Reinforcement Learning Under Latent Dynamics: Toward Statistical and Algorithmic Modularity. NeurIPS Philip Amortila, Dylan J. Foster, Nan Jiang, Akshay Krishnamurthy, Zakaria Mhammedi
2024	J	`jnl`	Reinforcement Learning under Latent Dynamics: Toward Statistical and Algorithmic Modularity. CoRR Philip Amortila, Dylan J. Foster, Nan Jiang, Akshay Krishnamurthy, Zakaria Mhammedi
2024	—	`conf`	Word Embeddings Are Steers for Language Models. ACL (1) Chi Han, Jialiang Xu, Manling Li, Yi Fung, Chenkai Sun, Nan Jiang, Tarek F. Abdelzaher, Heng Ji
2023	A*	`conf`	Adversarial Model for Offline Reinforcement Learning. NeurIPS Mohak Bhardwaj, Tengyang Xie, Byron Boots, Nan Jiang, Ching-An Cheng
2023	J	`jnl`	Adversarial Model for Offline Reinforcement Learning. CoRR Mohak Bhardwaj, Tengyang Xie, Byron Boots, Nan Jiang, Ching-An Cheng
2023	Misc	`conf`	Extended Abstract: Learning in Low-rank MDPs with Density Features. CISS Audrey Huang, Jinglin Chen, Nan Jiang
2023	A*	`conf`	Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. NeurIPS Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang, Nathan Kallus, Chengchun Shi, Wen Sun
2023	J	`jnl`	Gibbs Sampling from Human Feedback: A Provable KL- constrained Framework for RLHF. CoRR Wei Xiong, Hanze Dong, Chenlu Ye, Han Zhong, Nan Jiang, Tong Zhang
2023	J	`jnl`	LM-Switch: Lightweight Language Model Conditioning in Word Embedding Space. CoRR Chi Han, Jialiang Xu, Manling Li, Yi Fung, Chenkai Sun, Nan Jiang, Tarek F. Abdelzaher, Heng Ji
2023	J	`jnl`	Mitigating the Alignment Tax of RLHF. CoRR 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
2023	J	`jnl`	Reinforcement Learning in Low-Rank MDPs with Density Features. CoRR Audrey Huang, Jinglin Chen, Nan Jiang
2023	A*	`conf`	Reinforcement Learning in Low-rank MDPs with Density Features. ICML Audrey Huang, Jinglin Chen, Nan Jiang
2023	A*	`conf`	The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation. ICML Philip Amortila, Nan Jiang, Csaba Szepesvári
2023	J	`jnl`	The Optimal Approximation Factors in Misspecified Off-Policy Value Function Estimation. CoRR Philip Amortila, Nan Jiang, Csaba Szepesvári
2023	A*	`conf`	The Role of Coverage in Online Reinforcement Learning. ICLR Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang, Sham M. Kakade
2022	A*	`conf`	A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation. NeurIPS Philip Amortila, Nan Jiang, Dhruv Madeka, Dean P. Foster
2022	J	`jnl`	A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation. CoRR Philip Amortila, Nan Jiang, Dhruv Madeka, Dean P. Foster
2022	A*	`conf`	A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision Processes. ICML Chengchun Shi, Masatoshi Uehara, Jiawei Huang, Nan Jiang
2022	J	`jnl`	ARMOR: A Model-based Framework for Improving Arbitrary Baseline Policies with Offline Data. CoRR Tengyang Xie, Mohak Bhardwaj, Nan Jiang, Ching-An Cheng
2022	A*	`conf`	Adversarially Trained Actor Critic for Offline Reinforcement Learning. ICML Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal
2022	J	`jnl`	Adversarially Trained Actor Critic for Offline Reinforcement Learning. CoRR Ching-An Cheng, Tengyang Xie, Nan Jiang, Alekh Agarwal
2022	A*	`conf`	Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions. NeurIPS Audrey Huang, Nan Jiang
2022	J	`jnl`	Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions. CoRR Audrey Huang, Nan Jiang
2022	J	`jnl`	Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. CoRR Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang, Nathan Kallus, Chengchun Shi, Wen Sun
2022	A*	`conf`	Interaction-Grounded Learning with Action-Inclusive Feedback. NeurIPS Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan P. Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford
2022	J	`jnl`	Interaction-Grounded Learning with Action-inclusive Feedback. CoRR Tengyang Xie, Akanksha Saran, Dylan J. Foster, Lekan P. Molu, Ida Momennejad, Nan Jiang, Paul Mineiro, John Langford
2022	—	`conf`	Leveraging Synergies Between AI and Networking to Build Next Generation Edge Networks. CIC Sen Lin, Ming Shi, Anish Arora, Raef Bassily, Elisa Bertino, Constantine Caramanis, Kaushik R. Chowdhury, Eylem Ekici, Atilla Eryilmaz, Stratis Ioannidis, Nan Jiang, Gauri Joshi, Jim Kurose, Yingbin Liang, Zhiqiang Lin, Jia Liu, Mingyan Liu, Tommaso Melodia, Aryan Mokhtari, Rob Nowak, Sewoong Oh, Srini Parthasarathy, Chunyi Peng, Hulya Seferoglu, Ness B. Shroff, Sanjay Shakkottai, Kannan Srinivasan, Ameet Talwalkar, Aylin Yener, Lei Ying
2022	J	`jnl`	Offline Reinforcement Learning Under Value and Density-Ratio Realizability: the Power of Gaps. CoRR Jinglin Chen, Nan Jiang
2022	A*	`conf`	Offline Reinforcement Learning with Realizability and Single-policy Concentrability. COLT Wenhao Zhan, Baihe Huang, Audrey Huang, Nan Jiang, Jason D. Lee
2022	J	`jnl`	Offline Reinforcement Learning with Realizability and Single-policy Concentrability. CoRR Wenhao Zhan, Baihe Huang, Audrey Huang, Nan Jiang, Jason D. Lee
2022	A	`conf`	Offline reinforcement learning under value and density-ratio realizability: The power of gaps. UAI Jinglin Chen, Nan Jiang
2022	A	`conf`	On the Convergence Rate of Off-Policy Policy Optimization Methods with Density-Ratio Correction. AISTATS Jiawei Huang, Nan Jiang
2022	A*	`conf`	On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL. NeurIPS Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal
2022	J	`jnl`	On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL. CoRR Jinglin Chen, Aditya Modi, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal
2022	J	`jnl`	The Role of Coverage in Online Reinforcement Learning. CoRR Tengyang Xie, Dylan J. Foster, Yu Bai, Nan Jiang, Sham M. Kakade
2022	A*	`conf`	Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret. NeurIPS Jiawei Huang, Li Zhao, Tao Qin, Wei Chen, Nan Jiang, Tie-Yan Liu
2022	J	`jnl`	Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret. CoRR Jiawei Huang, Li Zhao, Tao Qin, Wei Chen, Nan Jiang, Tie-Yan Liu
2022	A*	`conf`	Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality. ICLR Jiawei Huang, Jinglin Chen, Li Zhao, Tao Qin, Nan Jiang, Tie-Yan Liu
2022	J	`jnl`	Towards Deployment-Efficient Reinforcement Learning: Lower Bound and Optimality. CoRR Jiawei Huang, Jinglin Chen, Li Zhao, Tao Qin, Nan Jiang, Tie-Yan Liu
2021	J	`jnl`	A Minimax Learning Approach to Off-Policy Evaluation in Partially Observable Markov Decision Processes. CoRR Chengchun Shi, Masatoshi Uehara, Nan Jiang
2021	A*	`conf`	Batch Value-function Approximation with Only Realizability. ICML Tengyang Xie, Nan Jiang
2021	A*	`conf`	Bellman-consistent Pessimism for Offline Reinforcement Learning. NeurIPS Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal
2021	J	`jnl`	Bellman-consistent Pessimism for Offline Reinforcement Learning. CoRR Tengyang Xie, Ching-An Cheng, Nan Jiang, Paul Mineiro, Alekh Agarwal
2021	—	`conf`	Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning. NeurIPS Datasets and Benchmarks Cameron Voloshin, Hoang Minh Le, Nan Jiang, Yisong Yue
2021	J	`jnl`	Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency. CoRR Masatoshi Uehara, Masaaki Imaizumi, Nan Jiang, Nathan Kallus, Wen Sun, Tengyang Xie
2021	A*	`conf`	Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration. AAAI Priyank Agrawal, Jinglin Chen, Nan Jiang
2021	A	`conf`	Minimax Model Learning. AISTATS Cameron Voloshin, Nan Jiang, Yisong Yue
2021	J	`jnl`	Minimax Model Learning. CoRR Cameron Voloshin, Nan Jiang, Yisong Yue
2021	J	`jnl`	Model-free Representation Learning and Exploration in Low-rank MDPs. CoRR Aditya Modi, Jinglin Chen, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal
2021	A*	`conf`	On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function. COLT Gellért Weisz, Philip Amortila, Barnabás Janzer, Yasin Abbasi-Yadkori, Nan Jiang, Csaba Szepesvári
2021	J	`jnl`	On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function. CoRR Gellért Weisz, Philip Amortila, Barnabás Janzer, Yasin Abbasi-Yadkori, Nan Jiang, Csaba Szepesvári
2021	J	`jnl`	On the Convergence Rate of Off-Policy Policy Optimization Methods with Density-Ratio Correction. CoRR Jiawei Huang, Nan Jiang
2021	A*	`conf`	Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning. NeurIPS Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, Yu Bai
2021	J	`jnl`	Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning. CoRR Tengyang Xie, Nan Jiang, Huan Wang, Caiming Xiong, Yu Bai
2020	J	`jnl`	A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting. CoRR Philip Amortila, Nan Jiang, Tengyang Xie
2020	J	`jnl`	Batch Value-function Approximation with Only Realizability. CoRR Tengyang Xie, Nan Jiang
2020	A*	`conf`	From Importance Sampling to Doubly Robust Policy Gradient. ICML Jiawei Huang, Nan Jiang
2020	J	`jnl`	Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration. CoRR Priyank Agrawal, Jinglin Chen, Nan Jiang
2020	J	`jnl`	Minimax Confidence Interval for Off-Policy Evaluation and Policy Optimization. CoRR Nan Jiang, Jiawei Huang
2020	A*	`conf`	Minimax Value Interval for Off-Policy Evaluation and Policy Optimization. NeurIPS Nan Jiang, Jiawei Huang
2020	A*	`conf`	Minimax Weight and Q-Function Learning for Off-Policy Evaluation. ICML Masatoshi Uehara, Jiawei Huang, Nan Jiang
2020	J	`jnl`	Q CoRR Tengyang Xie, Nan Jiang
2020	A	`conf`	Q* Approximation Schemes for Batch Reinforcement Learning: A Theoretical Comparison. UAI Tengyang Xie, Nan Jiang
2020	A	`conf`	Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles. AISTATS Aditya Modi, Nan Jiang, Ambuj Tewari, Satinder Singh
2019	J	`jnl`	Empirical Study of Off-Policy Policy Evaluation for Reinforcement Learning. CoRR Cameron Voloshin, Hoang Minh Le, Nan Jiang, Yisong Yue
2019	J	`jnl`	From Importance Sampling to Doubly Robust Policy Gradient. CoRR Jiawei Huang, Nan Jiang
2019	A*	`conf`	Information-Theoretic Considerations in Batch Reinforcement Learning. ICML Jinglin Chen, Nan Jiang
2019	J	`jnl`	Information-Theoretic Considerations in Batch Reinforcement Learning. CoRR Jinglin Chen, Nan Jiang
2019	J	`jnl`	Minimax Weight and Q-Function Learning for Off-Policy Evaluation. CoRR Masatoshi Uehara, Nan Jiang
2019	A*	`conf`	Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches. COLT Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
2019	A*	`conf`	Provably Efficient Q-Learning with Low Switching Cost. NeurIPS Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang
2019	J	`jnl`	Provably Efficient Q-Learning with Low Switching Cost. CoRR Yu Bai, Tengyang Xie, Nan Jiang, Yu-Xiang Wang
2019	A*	`conf`	Provably efficient RL with Rich Observations via Latent State Decoding. ICML Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford
2019	J	`jnl`	Provably efficient RL with Rich Observations via Latent State Decoding. CoRR Simon S. Du, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal, Miroslav Dudík, John Langford
2019	J	`jnl`	Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles. CoRR Aditya Modi, Nan Jiang, Ambuj Tewari, Satinder Singh
2018	A*	`conf`	Completing State Representations using Spectral Learning. NeurIPS Nan Jiang, Alex Kulesza, Satinder Singh
2018	A*	`conf`	Hierarchical Imitation and Reinforcement Learning. ICML Hoang Minh Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III
2018	J	`jnl`	Hierarchical Imitation and Reinforcement Learning. CoRR Hoang Minh Le, Nan Jiang, Alekh Agarwal, Miroslav Dudík, Yisong Yue, Hal Daumé III
2018	B	`conf`	Markov Decision Processes with Continuous Side Information. ALT Aditya Modi, Nan Jiang, Satinder Singh, Ambuj Tewari
2018	J	`jnl`	Model-Based Reinforcement Learning in Contextual Decision Processes. CoRR Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford
2018	A*	`conf`	On Oracle-Efficient PAC RL with Rich Observations. NeurIPS Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
2018	J	`jnl`	On Polynomial Time PAC Reinforcement Learning with Rich Observations. CoRR Christoph Dann, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
2018	A*	`conf`	Open Problem: The Dependence of Sample Complexity Lower Bounds on Planning Horizon. COLT Nan Jiang, Alekh Agarwal
2017	A*	`conf`	Contextual Decision Processes with low Bellman rank are PAC-Learnable. ICML Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
2017	J	`jnl`	Markov Decision Processes with Continuous Side Information. CoRR Aditya Modi, Nan Jiang, Satinder Singh, Ambuj Tewari
2017	—	`conf`	Repeated Inverse Reinforcement Learning. NIPS Kareem Amin, Nan Jiang, Satinder Singh
2017	J	`jnl`	Repeated Inverse Reinforcement Learning. CoRR Kareem Amin, Nan Jiang, Satinder Singh
2016	J	`jnl`	Contextual Decision Processes with Low Bellman Rank are PAC-Learnable. CoRR Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, Robert E. Schapire
2016	A*	`conf`	Improving Predictive State Representations via Gradient Descent. AAAI Nan Jiang, Alex Kulesza, Satinder Singh
2016	A*	`conf`	On Structural Properties of MDPs that Bound Loss Due to Shallow Planning. IJCAI Nan Jiang, Satinder Singh, Ambuj Tewari
2016	A*	`conf`	The Dependence of Effective Planning Horizon on Model Accuracy. IJCAI Nan Jiang, Alex Kulesza, Satinder Singh, Richard L. Lewis
2015	A*	`conf`	Abstraction Selection in Model-based Reinforcement Learning. ICML Nan Jiang, Alex Kulesza, Satinder Singh
2015	A	`conf`	Low-Rank Spectral Learning with Weighted Loss Functions. AISTATS Alex Kulesza, Nan Jiang, Satinder Singh
2015	A*	`conf`	Spectral Learning of Predictive State Representations with Insufficient Statistics. AAAI Alex Kulesza, Nan Jiang, Satinder Singh
2015	A	`conf`	The Dependence of Effective Planning Horizon on Model Accuracy. AAMAS Nan Jiang, Alex Kulesza, Satinder Singh, Richard L. Lewis
2014	A	`conf`	Improving UCT planning via approximate homomorphisms. AAMAS Nan Jiang, Satinder Singh, Richard L. Lewis