Lihong Li - RankMe

179 papers A* 47A 18B 1Misc 1Journal 88Unranked 23

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	HeaPA: Difficulty-Aware Heap Sampling and On-Policy Query Augmentation for LLM Reinforcement Learning. CoRR Weiqi Wang, Xin Liu, Binxuan Huang, Hejie Cui, Rongzhi Zhang, Changlong Yu, Shuowei Jin, Jingfeng Yang, Qingyu Yin, Zhengyang Wang, Zheng Li, Yifan Gao, Priyanka Nigam, Bing Yin, Lihong Li, Yangqiu Song
2025	J	`jnl`	Ask a Strong LLM Judge when Your Reward Model is Uncertain. CoRR Zhenghao Xu, Qin Lu, Qingru Zhang, Liang Qiu, Ilgee Hong, Changlong Yu, Wenlin Yao, Yao Liu, Haoming Jiang, Lihong Li, Hyokun Yun, Tuo Zhao
2025	J	`jnl`	Improving Sampling Efficiency in RLVR through Adaptive Rollout and Response Reuse. CoRR Yuheng Zhang, Wenlin Yao, Changlong Yu, Yao Liu, Qingyu Yin, Bing Yin, Hyokun Yun, Lihong Li
2025	J	`jnl`	SFT Doesn't Always Hurt General Capabilities: Revisiting Domain-Specific Fine-Tuning in LLMs. CoRR Jiacheng Lin, Zhongruo Wang, Kun Qian, Tian Wang, Arvind Srinivasan, Hansi Zeng, Ruochen Jiao, Xie Zhou, Jiri Gesi, Dakuo Wang, Yufan Guo, Kai Zhong, Weiqi Zhang, Sujay Sanghavi, Changyou Chen, Hyokun Yun, Lihong Li
2025	J	`jnl`	Self-Rewarding PPO: Aligning Large Language Models with Demonstrations Only. CoRR Qingru Zhang, Liang Qiu, Ilgee Hong, Zhenghao Xu, Tianyi Liu, Shiyang Li, Rongzhi Zhang, Zheng Li, Lihong Li, Bing Yin, Chao Zhang, Jianshu Chen, Haoming Jiang, Tuo Zhao
2025	A*	`conf`	WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning. EMNLP Zhepei Wei, Wenlin Yao, Yao Liu, Weizhi Zhang, Qin Lu, Liang Qiu, Changlong Yu, Puyang Xu, Chao Zhang, Bing Yin, Hyokun Yun, Lihong Li
2025	J	`jnl`	WebAgent-R1: Training Web Agents via End-to-End Multi-Turn Reinforcement Learning. CoRR Zhepei Wei, Wenlin Yao, Yao Liu, Weizhi Zhang, Qin Lu, Liang Qiu, Changlong Yu, Puyang Xu, Chao Zhang, Bing Yin, Hyokun Yun, Lihong Li
2022	J	`jnl`	A Reinforcement Learning Approach to Estimating Long-term Treatment Effects. CoRR Ziyang Tang, Yiheng Duan, Stephanie Zhang, Lihong Li
2022	A	`conf`	Estimating Long-term Effects from Experimental Data. RecSys Ziyang Tang, Yiheng Duan, Steven Zhu, Stephanie Zhang, Lihong Li
2022	J	`jnl`	Offline Policy Optimization in RL with Variance Regularizaton. CoRR Riashat Islam, Samarth Sinha, Homanga Bharadhwaj, Samin Yeasar Arnob, Zhuoran Yang, Animesh Garg, Zhaoran Wang, Lihong Li, Doina Precup
2022	A*	`conf`	Understanding Domain Randomization for Sim-to-real Transfer. ICLR Xiaoyu Chen, Jiachen Hu, Chi Jin, Lihong Li, Liwei Wang
2021	J	`jnl`	A Map of Bandits for E-commerce. CoRR Yi Liu, Lihong Li
2021	A*	`conf`	Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL. ICLR Xiaoyu Chen, Jiachen Hu, Lihong Li, Liwei Wang
2021	J	`jnl`	Guest editorial: special issue on reinforcement learning for real life. Mach. Learn. Yuxi Li, Alborz Geramifard, Lihong Li, Csaba Szepesvári, Tao Wang
2021	A*	`conf`	Near-Optimal Representation Learning for Linear Bandits and Linear RL. ICML Jiachen Hu, Xiaoyu Chen, Chi Jin, Lihong Li, Liwei Wang
2021	J	`jnl`	Near-optimal Representation Learning for Linear Bandits and Linear RL. CoRR Jiachen Hu, Xiaoyu Chen, Chi Jin, Lihong Li, Liwei Wang
2021	A*	`conf`	Neural Thompson Sampling. ICLR Weitong Zhang, Dongruo Zhou, Lihong Li, Quanquan Gu
2021	A	`conf`	Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders. AISTATS Andrew Bennett, Nathan Kallus, Lihong Li, Ali Mousavi
2021	A*	`conf`	On the Optimality of Batch Policy Optimization Algorithms. ICML Chenjun Xiao, Yifan Wu, Jincheng Mei, Bo Dai, Tor Lattimore, Lihong Li, Csaba Szepesvári, Dale Schuurmans
2021	J	`jnl`	On the Optimality of Batch Policy Optimization Algorithms. CoRR Chenjun Xiao, Yifan Wu, Tor Lattimore, Bo Dai, Jincheng Mei, Lihong Li, Csaba Szepesvári, Dale Schuurmans
2021	J	`jnl`	Understanding Domain Randomization for Sim-to-real Transfer. CoRR Xiaoyu Chen, Jiachen Hu, Chi Jin, Lihong Li, Liwei Wang
2020	A*	`conf`	Batch Stationary Distribution Estimation. ICML Junfeng Wen, Bo Dai, Lihong Li, Dale Schuurmans
2020	J	`jnl`	Batch Stationary Distribution Estimation. CoRR Junfeng Wen, Bo Dai, Lihong Li, Dale Schuurmans
2020	A*	`conf`	Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning. ICLR Ali Mousavi, Lihong Li, Qiang Liu, Denny Zhou
2020	J	`jnl`	Black-box Off-policy Estimation for Infinite-Horizon Reinforcement Learning. CoRR Ali Mousavi, Lihong Li, Qiang Liu, Denny Zhou
2020	A*	`conf`	CoinDICE: Off-Policy Confidence Interval Estimation. NeurIPS Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvári, Dale Schuurmans
2020	J	`jnl`	CoinDICE: Off-Policy Confidence Interval Estimation. CoRR Bo Dai, Ofir Nachum, Yinlam Chow, Lihong Li, Csaba Szepesvári, Dale Schuurmans
2020	J	`jnl`	Data Efficient Training for Reinforcement Learning with Adaptive Behavior Policy Sharing. CoRR Ge Liu, Rui Wu, Heng-Tze Cheng, Jing Wang, Jayden Ooi, Lihong Li, Ang Li, Wai Lok Sibon Li, Craig Boutilier, Ed H. Chi
2020	A*	`conf`	Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation. ICLR Ziyang Tang, Yihao Feng, Lihong Li, Dengyong Zhou, Qiang Liu
2020	J	`jnl`	Efficient Reinforcement Learning in Factored MDPs with Application to Constrained RL. CoRR Xiaoyu Chen, Jiachen Hu, Lihong Li, Liwei Wang
2020	A*	`conf`	Escaping the Gravitational Pull of Softmax. NeurIPS Jincheng Mei, Chenjun Xiao, Bo Dai, Lihong Li, Csaba Szepesvári, Dale Schuurmans
2020	A*	`conf`	GenDICE: Generalized Offline Estimation of Stationary Values. ICLR Ruiyi Zhang, Bo Dai, Lihong Li, Dale Schuurmans
2020	J	`jnl`	GenDICE: Generalized Offline Estimation of Stationary Values. CoRR Ruiyi Zhang, Bo Dai, Lihong Li, Dale Schuurmans
2020	A*	`conf`	Neural Contextual Bandits with UCB-based Exploration. ICML Dongruo Zhou, Lihong Li, Quanquan Gu
2020	J	`jnl`	Neural Thompson Sampling. CoRR Weitong Zhang, Dongruo Zhou, Lihong Li, Quanquan Gu
2020	A*	`conf`	Off-Policy Evaluation via the Regularized Lagrangian. NeurIPS Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, Dale Schuurmans
2020	J	`jnl`	Off-Policy Evaluation via the Regularized Lagrangian. CoRR Mengjiao Yang, Ofir Nachum, Bo Dai, Lihong Li, Dale Schuurmans
2020	J	`jnl`	Off-policy Evaluation in Infinite-Horizon Reinforcement Learning with Latent Confounders. CoRR Andrew Bennett, Nathan Kallus, Lihong Li, Ali Mousavi
2020	A	`conf`	Randomized Exploration in Generalized Linear Bandits. AISTATS Branislav Kveton, Manzil Zaheer, Csaba Szepesvári, Lihong Li, Mohammad Ghavamzadeh, Craig Boutilier
2019	A*	`conf`	A Kernel Loss for Solving the Bellman Equation. NeurIPS Yihao Feng, Lihong Li, Qiang Liu
2019	J	`jnl`	A Kernel Loss for Solving the Bellman Equation. CoRR Yihao Feng, Lihong Li, Qiang Liu
2019	J	`jnl`	A perspective on off-policy evaluation in reinforcement learning. Frontiers Comput. Sci. Lihong Li
2019	J	`jnl`	AlgaeDICE: Policy Gradient from Arbitrary Experience. CoRR Ofir Nachum, Bo Dai, Ilya Kostrikov, Yinlam Chow, Lihong Li, Dale Schuurmans
2019	J	`jnl`	Doubly Robust Bias Reduction in Infinite Horizon Off-Policy Estimation. CoRR Ziyang Tang, Yihao Feng, Lihong Li, Dengyong Zhou, Qiang Liu
2019	A*	`conf`	DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections. NeurIPS Ofir Nachum, Yinlam Chow, Bo Dai, Lihong Li
2019	J	`jnl`	DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections. CoRR Ofir Nachum, Yinlam Chow, Bo Dai, Lihong Li
2019	J	`jnl`	Neural Approaches to Conversational AI. Found. Trends Inf. Retr. Jianfeng Gao, Michel Galley, Lihong Li
2019	J	`jnl`	Neural Contextual Bandits with Upper Confidence Bound-Based Exploration. CoRR Dongruo Zhou, Lihong Li, Quanquan Gu
2019	—	`conf`	Neural Logic Machines. ICLR (Poster) Honghua Dong, Jiayuan Mao, Tian Lin, Chong Wang, Lihong Li, Denny Zhou
2019	J	`jnl`	Neural Logic Machines. CoRR Honghua Dong, Jiayuan Mao, Tian Lin, Chong Wang, Lihong Li, Denny Zhou
2019	A*	`conf`	Policy Certificates: Towards Accountable Reinforcement Learning. ICML Christoph Dann, Lihong Li, Wei Wei, Emma Brunskill
2019	J	`jnl`	Randomized Exploration in Generalized Linear Bandits. CoRR Branislav Kveton, Manzil Zaheer, Csaba Szepesvári, Lihong Li, Mohammad Ghavamzadeh, Craig Boutilier
2018	A*	`conf`	Adversarial Attacks on Stochastic Bandits. NeurIPS Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Xiaojin (Jerry) Zhu
2018	J	`jnl`	Adversarial Attacks on Stochastic Bandits. CoRR Kwang-Sung Jun, Lihong Li, Yuzhe Ma, Xiaojin Zhu
2018	A*	`conf`	BBQ-Networks: Efficient Exploration in Deep Reinforcement Learning for Task-Oriented Dialogue Systems. AAAI Zachary C. Lipton, Xiujun Li, Jianfeng Gao, Lihong Li, Faisal Ahmed, Li Deng
2018	—	`conf`	Boosting the Actor with Dual Critic. ICLR (Poster) Bo Dai, Albert E. Shaw, Niao He, Lihong Li, Le Song
2018	A*	`conf`	Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation. NeurIPS Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou
2018	J	`jnl`	Breaking the Curse of Horizon: Infinite-Horizon Off-Policy Estimation. CoRR Qiang Liu, Lihong Li, Ziyang Tang, Dengyong Zhou
2018	—	`conf`	Data Poisoning Attacks in Contextual Bandits. GameSec Yuzhe Ma, Kwang-Sung Jun, Lihong Li, Xiaojin Zhu
2018	J	`jnl`	Data Poisoning Attacks in Contextual Bandits. CoRR Yuzhe Ma, Kwang-Sung Jun, Lihong Li, Xiaojin Zhu
2018	—	`conf`	Neural Approaches to Conversational AI. ACL (5) Jianfeng Gao, Michel Galley, Lihong Li
2018	A*	`conf`	Neural Approaches to Conversational AI. SIGIR Jianfeng Gao, Michel Galley, Lihong Li
2018	J	`jnl`	Neural Approaches to Conversational AI. CoRR Jianfeng Gao, Michel Galley, Lihong Li
2018	J	`jnl`	Policy Certificates: Towards Accountable Reinforcement Learning. CoRR Christoph Dann, Lihong Li, Wei Wei, Emma Brunskill
2018	A*	`conf`	SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation. ICML Bo Dai, Albert E. Shaw, Lihong Li, Lin Xiao, Niao He, Zhen Liu, Jianshu Chen, Le Song
2018	A*	`conf`	Scalable Bilinear Learning Using State and Action Features. ICML Yichen Chen, Lihong Li, Mengdi Wang
2018	J	`jnl`	Scalable Bilinear π Learning Using State and Action Features. CoRR Yichen Chen, Lihong Li, Mengdi Wang
2018	A*	`conf`	Subgoal Discovery for Hierarchical Dialogue Policy Learning. EMNLP Da Tang, Xiujun Li, Jianfeng Gao, Chong Wang, Lihong Li, Tony Jebara
2018	J	`jnl`	Subgoal Discovery for Hierarchical Dialogue Policy Learning. CoRR Da Tang, Xiujun Li, Jianfeng Gao, Chong Wang, Lihong Li, Tony Jebara
2017	J	`jnl`	Boosting the Actor with Dual Critic. CoRR Bo Dai, Albert E. Shaw, Niao He, Lihong Li, Le Song
2017	A*	`conf`	Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning. EMNLP Baolin Peng, Xiujun Li, Lihong Li, Jianfeng Gao, Asli Celikyilmaz, Sungjin Lee, Kam-Fai Wong
2017	J	`jnl`	Composite Task-Completion Dialogue System via Hierarchical Deep Reinforcement Learning. CoRR Baolin Peng, Xiujun Li, Lihong Li, Jianfeng Gao, Asli Celikyilmaz, Sungjin Lee, Kam-Fai Wong
2017	—	`conf`	End-to-End Task-Completion Neural Dialogue Systems. IJCNLP(1) Xiujun Li, Yun-Nung Chen, Lihong Li, Jianfeng Gao, Asli Celikyilmaz
2017	J	`jnl`	End-to-End Task-Completion Neural Dialogue Systems. CoRR Xiujun Li, Yun-Nung Chen, Lihong Li, Jianfeng Gao
2017	J	`jnl`	Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems. CoRR Xiujun Li, Yun-Nung Chen, Lihong Li, Jianfeng Gao, Asli Celikyilmaz
2017	—	`conf`	Neuro-Symbolic Program Synthesis. ICLR (Poster) Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, Pushmeet Kohli
2017	J	`jnl`	Provable Optimal Algorithms for Generalized Linear Contextual Bandits. CoRR Lihong Li, Yu Lu, Dengyong Zhou
2017	A*	`conf`	Provably Optimal Algorithms for Generalized Linear Contextual Bandits. ICML Lihong Li, Yu Lu, Dengyong Zhou
2017	—	`conf`	Q-LDA: Uncovering Latent Patterns in Text-based Sequential Decision Processes. NIPS Jianshu Chen, Chong Wang, Lin Xiao, Ji He, Lihong Li, Li Deng
2017	J	`jnl`	Scaffolding Networks for Teaching and Learning to Comprehend. CoRR Asli Celikyilmaz, Li Deng, Lihong Li, Chong Wang
2017	J	`jnl`	Smoothed Dual Embedding Control. CoRR Bo Dai, Albert E. Shaw, Lihong Li, Lin Xiao, Niao He, Jianshu Chen, Le Song
2017	A*	`conf`	Stochastic Variance Reduction Methods for Policy Evaluation. ICML Simon S. Du, Jianshu Chen, Lihong Li, Lin Xiao, Dengyong Zhou
2017	J	`jnl`	Stochastic Variance Reduction Methods for Policy Evaluation. CoRR Simon S. Du, Jianshu Chen, Lihong Li, Lin Xiao, Dengyong Zhou
2017	—	`conf`	Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access. ACL (1) Bhuwan Dhingra, Lihong Li, Xiujun Li, Jianfeng Gao, Yun-Nung Chen, Faisal Ahmed, Li Deng
2016	J	`jnl`	A User Simulator for Task-Completion Dialogues. CoRR Xiujun Li, Zachary C. Lipton, Bhuwan Dhingra, Lihong Li, Jianfeng Gao, Yun-Nung Chen
2016	—	`conf`	Active Learning with Oracle Epiphany. NIPS Tzu-Kuo Huang, Lihong Li, Ara Vartanian, Saleema Amershi, Xiaojin Zhu
2016	A*	`conf`	An efficient algorithm for contextual bandits with knapsacks, and an extension to concave objectives. COLT Shipra Agrawal, Nikhil R. Devanur, Lihong Li
2016	A*	`conf`	Click-based Hot Fixes for Underperforming Torso Queries. SIGIR Masrour Zoghi, Tomás Tunys, Lihong Li, Damien Jose, Junyan Chen, Chun Ming Chin, Maarten de Rijke
2016	J	`jnl`	Combating Reinforcement Learning's Sisyphean Curse with Intrinsic Fear. CoRR Zachary C. Lipton, Jianfeng Gao, Lihong Li, Jianshu Chen, Li Deng
2016	A*	`conf`	Deep Reinforcement Learning with a Combinatorial Action Space for Predicting Popular Reddit Threads. EMNLP Ji He, Mari Ostendorf, Xiaodong He, Jianshu Chen, Jianfeng Gao, Lihong Li, Li Deng
2016	J	`jnl`	Deep Reinforcement Learning with a Combinatorial Action Space for Predicting and Tracking Popular Discussion Threads. CoRR Ji He, Mari Ostendorf, Xiaodong He, Jianshu Chen, Jianfeng Gao, Lihong Li, Li Deng
2016	—	`conf`	Deep Reinforcement Learning with a Natural Language Action Space. ACL (1) Ji He, Jianshu Chen, Xiaodong He, Jianfeng Gao, Lihong Li, Li Deng, Mari Ostendorf
2016	A*	`conf`	Doubly Robust Off-policy Value Evaluation for Reinforcement Learning. ICML Nan Jiang, Lihong Li
2016	J	`jnl`	Efficient Exploration for Dialog Policy Learning with Deep BBQ Networks \& Replay Buffer Spiking. CoRR Zachary C. Lipton, Jianfeng Gao, Lihong Li, Xiujun Li, Faisal Ahmed, Li Deng
2016	J	`jnl`	End-to-End Reinforcement Learning of Dialogue Agents for Information Access. CoRR Bhuwan Dhingra, Lihong Li, Xiujun Li, Jianfeng Gao, Yun-Nung Chen, Faisal Ahmed, Li Deng
2016	J	`jnl`	Neuro-Symbolic Program Synthesis. CoRR Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, Pushmeet Kohli
2016	B	`conf`	On the Prior Sensitivity of Thompson Sampling. ALT Che-Yu Liu, Lihong Li
2016	J	`jnl`	Online Evaluation for Information Retrieval. Found. Trends Inf. Retr. Katja Hofmann, Lihong Li, Filip Radlinski
2015	J	`jnl`	Contextual Bandits with Global Constraints and Objective. CoRR Shipra Agrawal, Nikhil R. Devanur, Lihong Li
2015	—	`conf`	Counterfactual Estimation and Optimization of Click Metrics in Search Engines: A Case Study. WWW (Companion Volume) Lihong Li, Shunbao Chen, Jim Kleban, Ankur Gupta
2015	J	`jnl`	Deep Reinforcement Learning with an Unbounded Action Space. CoRR Ji He, Jianshu Chen, Xiaodong He, Jianfeng Gao, Lihong Li, Li Deng, Mari Ostendorf
2015	J	`jnl`	Doubly Robust Off-policy Evaluation for Reinforcement Learning. CoRR Nan Jiang, Lihong Li
2015	J	`jnl`	Doubly Robust Policy Evaluation and Optimization. CoRR Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li
2015	J	`jnl`	Evaluation of Explore-Exploit Policies in Multi-result Ranking Systems. CoRR Dragomir Yankov, Pavel Berkhin, Lihong Li
2015	A	`conf`	Offline Evaluation and Optimization for Interactive Systems. WSDM Lihong Li
2015	J	`jnl`	On the Prior Sensitivity of Thompson Sampling. CoRR Che-Yu Liu, Lihong Li
2015	J	`jnl`	Recurrent Reinforcement Learning: A Hybrid Approach. CoRR Xiujun Li, Lihong Li, Jianfeng Gao, Xiaodong He, Jianshu Chen, Li Deng, Ji He
2015	J	`jnl`	The Online Discovery Problem and Its Application to Lifelong Reinforcement Learning. CoRR Emma Brunskill, Lihong Li
2015	A	`conf`	Toward Minimax Off-policy Value Estimation. AISTATS Lihong Li, Rémi Munos, Csaba Szepesvári
2015	A	`conf`	Toward Predicting the Outcome of an A/B Experiment for Search Relevance. WSDM Lihong Li, Jin Young Kim, Imed Zitouni
2014	J	`jnl`	Counterfactual Estimation and Optimization of Click Metrics for Search Engines. CoRR Lihong Li, Shunbao Chen, Jim Kleban, Ankur Gupta
2014	J	`jnl`	Exploiting User Preference for Online Learning in Web Content Optimization Systems. ACM Trans. Intell. Syst. Technol. Jiang Bian, Bo Long, Lihong Li, Taesup Moon, Anlei Dong, Yi Chang
2014	J	`jnl`	On Minimax Optimal Offline Policy Evaluation. CoRR Lihong Li, Rémi Munos, Csaba Szepesvári
2014	A*	`conf`	PAC-inspired Option Discovery in Lifelong Reinforcement Learning. ICML Emma Brunskill, Lihong Li
2014	A*	`conf`	Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. ICML Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
2014	J	`jnl`	Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits. CoRR Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, Robert E. Schapire
2014	—	`conf`	Temporal supervised learning for inferring a dialog policy from example conversations. SLT Lihong Li, He He, Jason D. Williams
2013	J	`jnl`	Efficient Online Bootstrapping for Large Scale Learning. CoRR Zhen Qin, Vaclav Petricek, Nikos Karampatziakis, Lihong Li, John Langford
2013	J	`jnl`	Generalized Thompson Sampling for Contextual Bandits. CoRR Lihong Li
2013	A	`conf`	Sample Complexity of Multi-task Reinforcement Learning. UAI Emma Brunskill, Lihong Li
2013	J	`jnl`	Sample Complexity of Multi-task Reinforcement Learning. CoRR Emma Brunskill, Lihong Li
2012	J	`jnl`	A Bayesian Sampling Approach to Exploration in Reinforcement Learning CoRR John Asmuth, Lihong Li, Michael L. Littman, Ali Nouri, David Wingate
2012	J	`jnl`	An Online Learning Framework for Refining Recency Search Results with User Click Feedback. ACM Trans. Inf. Syst. Taesup Moon, Wei Chu, Lihong Li, Zhaohui Zheng, Yi Chang
2012	A	`conf`	Attention and Selection in Online Choice Tasks. UMAP Vidhya Navalpakkam, Ravi Kumar, Lihong Li, D. Sivakumar
2012	—	`conf`	Bandits with Generalized Linear Models. ICML On-line Trading of Exploration and Exploitation Lihong Li, Wei Chu, John Langford, Taesup Moon, Xuanhui Wang
2012	J	`jnl`	CORL: A Continuous-state Offset-dynamics Reinforcement Learner CoRR Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy
2012	J	`jnl`	Cloud control: voluntary admission control for intranet traffic management. Inf. Syst. E Bus. Manag. John Langford, Lihong Li, R. Preston McAfee, Kishore Papineni
2012	J	`jnl`	Incremental Model-based Learners With Formal Learning-Time Guarantees. CoRR Alexander L. Strehl, Lihong Li, Michael L. Littman
2012	A*	`conf`	Joint relevance and freshness learning from clickthroughs for news search. WWW Hongning Wang, Anlei Dong, Lihong Li, Yi Chang, Evgeniy Gabrilovich
2012	A*	`conf`	Open Problem: Regret Bounds for Thompson Sampling. COLT Lihong Li, Olivier Chapelle
2012	—	`ch.`	Sample Complexity Bounds of Exploration. Reinforcement Learning Lihong Li
2012	J	`jnl`	Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits CoRR Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li
2012	A	`conf`	Sample-efficient Nonstationary Policy Evaluation for Contextual Bandits. UAI Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li
2011	—	`conf`	An Empirical Evaluation of Thompson Sampling. NIPS Olivier Chapelle, Lihong Li
2011	A	`conf`	Contextual Bandit Algorithms with Supervised Learning Guarantees. AISTATS Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire
2011	A	`conf`	Contextual Bandits with Linear Payoff Functions. AISTATS Wei Chu, Lihong Li, Lev Reyzin, Robert E. Schapire
2011	J	`jnl`	Doubly Robust Policy Evaluation and Learning CoRR Miroslav Dudík, John Langford, Lihong Li
2011	A*	`conf`	Doubly Robust Policy Evaluation and Learning. ICML Miroslav Dudík, John Langford, Lihong Li
2011	J	`jnl`	Knows what it knows: a framework for self-aware learning. Mach. Learn. Lihong Li, Michael L. Littman, Thomas J. Walsh, Alexander L. Strehl
2011	A	`conf`	Linear-Time Estimators for Propensity Scores. AISTATS Deepak Agarwal, Lihong Li, Alexander J. Smola
2011	J	`jnl`	Refining Recency Search Results with User Click Feedback CoRR Taesup Moon, Wei Chu, Lihong Li, Zhaohui Zheng, Yi Chang
2011	A	`conf`	Unbiased offline evaluation of contextual-bandit-based news article recommendation algorithms. WSDM Lihong Li, Wei Chu, John Langford, Xuanhui Wang
2011	A*	`conf`	Unbiased online active learning in data streams. KDD Wei Chu, Martin Zinkevich, Lihong Li, Achint Thomas, Belle L. Tseng
2010	J	`jnl`	A Contextual-Bandit Approach to Personalized News Article Recommendation CoRR Lihong Li, Wei Chu, John Langford, Robert E. Schapire
2010	A*	`conf`	A contextual-bandit approach to personalized news article recommendation. WWW Lihong Li, Wei Chu, John Langford, Robert E. Schapire
2010	J	`jnl`	An Optimal High Probability Algorithm for the Contextual Bandit Problem CoRR Alina Beygelzimer, John Langford, Lihong Li, Lev Reyzin, Robert E. Schapire
2010	J	`jnl`	An Unbiased, Data-Driven, Offline Evaluation Method of Contextual Bandit Algorithms CoRR Lihong Li, Wei Chu, John Langford
2010	—	`conf`	Learning from Logged Implicit Exploration Data. NIPS Alexander L. Strehl, John Langford, Lihong Li, Sham M. Kakade
2010	J	`jnl`	Maintaining Equilibria During Exploration in Sponsored Search Auctions. Algorithmica John Langford, Lihong Li, Yevgeniy Vorobeychik, Jennifer Wortman
2010	A	`conf`	Online learning for recency search ranking using real-time user feedback. CIKM Taesup Moon, Lihong Li, Wei Chu, Ciya Liao, Zhaohui Zheng, Yi Chang
2010	—	`conf`	Parallelized Stochastic Gradient Descent. NIPS Martin Zinkevich, Markus Weimer, Alexander J. Smola, Lihong Li
2010	J	`jnl`	Reducing reinforcement learning to KWIK online regression. Ann. Math. Artif. Intell. Lihong Li, Michael L. Littman
2009	A	`conf`	A Bayesian Sampling Approach to Exploration in Reinforcement Learning. UAI John Asmuth, Lihong Li, Michael L. Littman, Ali Nouri, David Wingate
2009	J	`jnl`	Learning and planning in environments with delayed feedback. Auton. Agents Multi Agent Syst. Thomas J. Walsh, Ali Nouri, Lihong Li, Michael L. Littman
2009	—	`conf`	Online exploration in least-squares policy iteration. AAMAS (2) Lihong Li, Michael L. Littman, Christopher R. Mansley
2009	J	`jnl`	Provably Efficient Learning with Typed Parametric Models. J. Mach. Learn. Res. Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy
2009	J	`jnl`	Reinforcement Learning in Finite MDPs: PAC Analysis. J. Mach. Learn. Res. Alexander L. Strehl, Lihong Li, Michael L. Littman
2009	A	`conf`	Reinforcement learning for dialog management using least-squares Policy iteration and fast feature selection. INTERSPEECH Lihong Li, Jason D. Williams, Suhrid Balakrishnan
2009	J	`jnl`	Sparse Online Learning via Truncated Gradient. J. Mach. Learn. Res. John Langford, Lihong Li, Tong Zhang
2009	A*	`conf`	The adaptive ICML Carlos Diuk, Lihong Li, Bethany R. Leffler
2009	A*	`conf`	Workshop summary: Results of the 2009 reinforcement learning competition. ICML David Wingate, Carlos Diuk, Lihong Li, Matthew Taylor, Jordan Frank
2008	A*	`conf`	A worst-case comparison between temporal difference and residual gradient with linear function approximation. ICML Lihong Li
2008	A*	`conf`	An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning. ICML Ronald Parr, Lihong Li, Gavin Taylor, Christopher Painter-Wakefield, Michael L. Littman
2008	A	`conf`	CORL: A Continuous-state Offset-dynamics Reinforcement Learner. UAI Emma Brunskill, Bethany R. Leffler, Lihong Li, Michael L. Littman, Nicholas Roy
2008	Misc	`conf`	Efficient Value-Function Approximation via Online Linear Regression. ISAIM Lihong Li, Michael L. Littman
2008	A*	`conf`	Knows what it knows: a framework for self-aware learning. ICML Lihong Li, Michael L. Littman, Thomas J. Walsh
2008	J	`jnl`	Sparse Online Learning via Truncated Gradient CoRR John Langford, Lihong Li, Tong Zhang
2008	—	`conf`	Sparse Online Learning via Truncated Gradient. NIPS John Langford, Lihong Li, Tong Zhang
2007	A*	`conf`	Analyzing feature generation for value-function approximation. ICML Ronald Parr, Christopher Painter-Wakefield, Lihong Li, Michael L. Littman
2007	J	`jnl`	Focus of Attention in Reinforcement Learning. J. Univers. Comput. Sci. Lihong Li, Vadim Bulitko, Russell Greiner
2007	—	`conf`	Maintaining Equilibria During Exploration in Sponsored Search Auctions. WINE Jennifer Wortman, Yevgeniy Vorobeychik, Lihong Li, John Langford
2007	—	`conf`	Planning and Learning in Environments with Delayed Feedback. ECML Thomas J. Walsh, Ali Nouri, Lihong Li, Michael L. Littman
2006	A	`conf`	Incremental Model-based Learners With Formal Learning-Time Guarantees. UAI Alexander L. Strehl, Lihong Li, Michael L. Littman
2006	A*	`conf`	PAC model-free reinforcement learning. ICML Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman
2006	—	`conf`	Towards a Unified Theory of State Abstraction for MDPs. AI&M Lihong Li, Thomas J. Walsh, Michael L. Littman
2005	A*	`conf`	Lazy Approximation for Solving Continuous Finite-Horizon MDPs. AAAI Lihong Li, Michael L. Littman
2004	—	`conf`	Batch Reinforcement Learning with State Importance. ECML Lihong Li, Vadim Bulitko, Russell Greiner
2003	A*	`conf`	Lookahead Pathologies for Single Agent Search. IJCAI Vadim Bulitko, Lihong Li, Russell Greiner, Ilya Levner
2003	—	`conf`	Towards Automated Creation of Image Interpretation Systems. Australian Conference on Artificial Intelligence Ilya Levner, Vadim Bulitko, Lihong Li, Greg Lee, Russell Greiner