Wen Sun
Department of Computer Science
Cornell University

Phone:Email:(919)451-3502
ws455@cornell.edu

Education
Ph.D. in Robotics, Robotics Institute, School of Computer Science, Carnegie Mellon University, USA.
M.S. Computer Science, University of North Carolina at Chapel Hill, USA, 2014.
B.S. Computer Science, Simon Fraser University, Canada, and Zhejiang University, China, 2012 (SFUand ZU Dual Degree).

Employment
July 2020 - present: Assistant Professor, Computer Science Department, Cornell University
July 2019 - June 2020: Postdoctoral Researcher, Microsoft Research at New York City
August 2014 - June 2019: Research Assistant, Robotics Institute, Carnegie Mellon UniversityPublications
Monograph
Alekh Agarwal, Nan Jiang, Sham Kakade, Wen Sun, “Reinforcement Learning: Theory and Algorithms" (link to the working draft)

Pre-Prints
Kaiwen Wang, Junxiong Wang, Yueying Li, Nathan Kallus, Immanuel Trummer, Wen Sun, JoinGym:
An Efficient Query Optimization Environment for Reinforcement Learning, arXiv, 2023
Jonathan D. Chang, Kiante Brantley, Rajkumar Ramamurthy, Dipendra Misra, Wen Sun, “Learning toGenerate Better Than Your LLM”, arXiv, 2023
Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee, “How to Query Human Feedback Efficientlyin RL”, arXiv, 2023
Wenhao Zhan, Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun, “Provable Offline Reinforcement Learning with Human Feedback”, arXiv, 2023
Masatoshi Uehara, Masaaki Imaizumi, Nan Jiang, Nathan Kallus, Wen Sun, Tengyang Xie, “FiniteSample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and FirstOrder Efficiency ", Under review for Annual of Statistics

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2Journal Publications
Thodoris Lykouris*, Max Simchowitz*, Aleksandrs Slivkins*, Wen Sun*, “Corruption Robust Exploration in Episodic Reinforcement Learning”, in Mathematics of Operations Research, 2023
Wen Sun, Jur van den Berg, Ron Alterovitz, “Stochastic Extended LQR: Optimization-based Motion
Planning under Uncertainty,” in IEEE. Transactions on Automation Science and Engineering (TASE), 2016.
Wen Sun, Sachin Patil, Ron Alterovitz, “High-Frequency Replanning Under Uncertainty Using Parallel
Sampling-Based Motion Planning,” in IEEE. Transactions on Robotics (TRO), 2015.

Refereed Conference Proceedings
Kaiwen Wang, Kevin Zhou, Runzhe Wu, Nathan Kallus, Wen Sun, “The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning”, Neural Information Processing Systems (NeurIPS),2023
Ayush Sekhari*, Karthik Sridharan*, Wen Sun*, Runzhe Wu*, Contextual Bandits and Imitation Learning via Preference-Based Active Queries, Neural Information Processing Systems (NeurIPS), 2023
Ayush Sekhari*, Karthik Sridharan*, Wen Sun*, Runzhe Wu*, Selective Sampling and Imitation Learning via Online Regression , Neural Information Processing Systems (NeurIPS), 2023
Masatoshi Uehara, Nathan Kallus, Jason D. Lee, Wen Sun, “Refined Value-Based Offline RL underRealizability and Partial Coverage”, Neural Information Processing Systems (NeurIPS), 2023
Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett, Victor Chernozhukov, Nan Jiang, NathanKallus, Chengchun Shi, Wen Sun, “Future-Dependent Value-Based Off-Policy Evaluation in POMDPs”, Neural Information Processing Systems (NeurIPS) 2023 (Spotlight)
Katie Z Luo, Zhenzhen Liu, Xiangyu Chen, Yurong You, Sagie Benaim, Cheng Perng Phoo, MarkCampbell, Wen Sun, Bharath Hariharan, Kilian Q Weinberger, “Reward Finetuning for Faster andMore Accurate Unsupervised Object Discovery", Neural Information Processing Systems (NeurIPS) 2023
Alekh Agarwal*, Yuda Song*, Wen Sun*, Kaiwen Wang*, Mengdi Wang*, Xuezhou Zhang*, “Provable Benefits of Representational Transfer in Reinforcement Learning ”, Conference on Learning Theory(COLT), 2023
Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun, “Computationally Efficient
PAC RL in POMDPs with Latent Determinism and Conditional Embeddings”, International Conference
on Machine Learning (ICML), 2023
Runzhe Wu, Masatoshi Uehara, Wen Sun, “Distributional Offline Policy Evaluation with Predictive
Error Guarantees ”, International Conference on Machine Learning (ICML), 2023
Yihan Du, Longbo Huang, Wen Sun, “Multi-task Representation Learning for Pure Exploration inLinear Bandits”, International Conference on Machine Learning (ICML), 2023
Kaiwen Wang, Nathan Kallus, Wen Sun, “Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR ”, International Conference on Machine Learning (ICML), 2023
Yuda Song, Yifei Zhou, Ayush Sekhari, J. Andrew Bagnell, Akshay Krishnamurthy, Wen Sun, “HybridRL: Using Both Offline and Online Data Can Make RL Efficient ”, International Conference on Learning
Representations (ICLR), 2023
Wenhao Zhan, Masatoshi Uehara, Wen Sun, Jason D. Lee, “PAC Reinforcement Learning for Predictive
State Representations”, International Conference on Learning Representations (ICLR), 2023

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3

Masatoshi Uehara, Ayush Sekhari, Jason D. Lee, Nathan Kallus, Wen Sun, “Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems ”, Neural Information Processing System(NeurIPS) 2022
Jonathan Chang, Kaiwen Wang, Nathan Kallus, Wen Sun, “ Learning Bellman Complete Representations for Offline Policy Evaluation”, International Conference on Machine Learning (ICML) 2022 (LongTalk)
Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang, Alekh Agarwal, Wen Sun, “Efficient
Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach”, International Conference on Machine Learning (ICML) 2022
Yurong You, Katie Z Luo, Cheng Perng Phoo, Wei-Lun Chao, Wen Sun, Bharath Hariharan, MarkCampbell, and Kilian Q. Weinberger, “Learning to Detect Mobile Objects from LiDAR Scans WithoutLabels”, The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2022
Anirudh Vemula, Wen Sun, Maxim Likhachev, Drew Bagnell, “On the Effectiveness of Iterative Learning Control ”, Learning for Dynamics & Control Conference (L4DC) 2022
Yuda Song, Ye Yuan, Wen Sun, Kris Kitani, “Online No-regret Model-Based Meta RL for PersonalizedNavigation ”, Learning for Dynamics & Control Conference (L4DC) 2022
Masatoshi Uehara, Xuezhou Zhang, Wen Sun, “Representation Learning for Online and Offline RL in
Low-rank MDPs”, International Conference on Learning Representations (ICLR) 2022 (Spotlight)
Masatoshi Uehara, Wen Sun, “Pessimistic Model-based Offline Reinforcement Learning under PartialCoverage”, International Conference on Learning Representations (ICLR) 2022
Ye Yuan, Yuda Song, Zhengyi Luo, Wen Sun, Kris Kitani, “Transform2Act: Learning a Transform-andControl Policy for Efficient Agent Design”, International Conference on Learning Representations (ICLR)
2022 (Oral)
Yurong You, Katie Luo, Xiangyu Chen, Junan Chen, Wei-Lun Chao, Wen Sun, Bharath Hariharan,Mark Campbell, and Kilian Q Weinberger, “Hindsight is 20/20: Leveraging Past Traversals to Aid 3D
Perception ”, International Conference on Learning Representations (ICLR) 2022
Xuezhou Zhang, Yiding Chen, Jerry Zhu, Wen Sun, “Corruption-Robust Offline Reinforcement Learning”, International Conference on Artifical Intelligence and Statistics (AISTATS) 2022
Jonathan Chang, Masatoshi Uehara, Dhruv Sreenivas, Rahul Kidambi, Wen Sun, “Mitigating Covariate
Shift in Imitation Learning via Offline Data Without Great Coverage ", in Neural Information Processing
Systems (NeurIPS) 2021
Rahul Kidambi, Jonathan Chang, Wen Sun, “MobILE: Model-Based Imitation Learning From Observation Alone”, in Neural Information Processing Systems (NeurIPS), 2021
Xuezhou Zhang, Yiding Chen, Xiaojin Zhu, Wen Sun ,“Robust Policy Gradient against Strong Data
Corruption ", International Conference on Machine Learning (ICML), 2021
Simon Du*, Sham M. Kakade*, Jason D. Lee*, Shachar Lovett*, Gaurav Mahajan*, Wen Sun*, RuosongWang, “Bilinear Classes: A Structural Framework for Provable Generalization in RL ", International
Conference on Machine Learning (ICML), 2021 (Long Talk)
Luke Wang, Yiwei Bai, Wen Sun, Thorsten Joachims,“Fairness of Exposure in Stochastic Bandits",
International Conference on Machine Learning (ICML), 2021

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4Yuda Song, Wen Sun, “PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided
Exploration", International Conference on Machine Learning (ICML), 2021
Thodoris Lykouris*, Max Simchowitz*, Aleksandrs Slivkins*, Wen Sun*, “Corruption robust exploration in episodic reinforcement learning", Conference on Learning Theory (COLT), 2021
Alekh Agarwal*, Mikael Henaff*, Sham Kakade*, Wen Sun*, “PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning,", in Neural Information Processing Systems (NeurIPS) 2020
Alekh Agarwal*, Sham Kakade*, Akshay Krishnamurthy*, Wen Sun*, “FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs," in Neural Information Processing Systems (NeurIPS)
2020 (Oral)
Sham Kakade*, Akshay Krishnamurthy*, Kendall Lowrey*, Motoya Ohnishi*, Wen Sun*, “Information Theoretic Regret Bounds for Online Nonlinear Control," in Neural Information Processing Systems(NeurIPS) 2020
Wenhao Luo, Wen Sun, Ashish Kapoor, “Multi-Robot Collision Avoidance under Uncertainty with
Probabilistic Safety Barrier Certificates,", in Neural Information Processing Systems (NeurIPS) 2020 (Spotlight)
Kiante Brantley*, Miroslav Dudik*, Thodoris Lykouris*, Sobhan Miryoosefi*, Max Simchowitz*, Aleksandrs Slivkins*, Wen Sun*, “Constrained Episodic Reinforcement Learning in Concave-convex andKnapsack Settings," in Neural Information Processing Systems (NeurIPS) 2020
Yuda Song, Aditi Mavalankar, Wen Sun, Sicun Gao, “Provably Efficient Model-based Policy Adaptation,” in International Conference on Machine Learning (ICML), 2020
Kiante Brantley, Wen Sun, Mikael Henaff, “Disagreement-Regularized Imitation Learning ,” in International Conference on Representation Learning (ICLR), 2020 (Spotlight)
Liyiming Ke, Sanjiban Choudhury, Matt Barnes, Wen Sun, Gilwoo Lee, Siddhartha Srinivasa, “ Imitation Learning as f-Divergence Minimization,” in International Workshop on the Algorithmic Foundations ofRobotics (WAFR), 2020
Huaian Diao*, Rajesh Jayaram*, Zhao Song*, Wen Sun*, David P. Woodruff*, “Optimal Sketching forKronecker Product Regression and Low Rank Approximation, ” in Neural Information Process Systems(NeurIPS), 2019
Yuzhe Ma, Xuezhou Zhang, Wen Sun, Jerry Zhu, “Policy Poisoning in Batch Reinforcement Learningand Control,” in Neural Information Process Systems (NeurIPS), 2019
Wen Sun, Anirudh Vemula, Byron Boots, J. Andrew Bagnell, “Provably Efficient Imitation Learningfrom Observation Alone, ” in International Conference on Machine Learning (ICML), 2019 (Long Talk)
Wen Sun, Alina Beygelzimer, Hal Daumé III, John Langford, Paul Mineiro, “Contextual Memory Tree,”in International Conference on Machine Learning (ICML), 2019 (Long Talk)
Wen Sun, Nan Jiang, Akshay Krishnamurthy, Alekh Agarwal, John Langford, “Model-based RL in
CDPs: PAC bounds and Exponential Improvements over Model-free Approaches,” in Conference onLearning Theory (COLT), 2019.
Anirudh Vemula, Wen Sun, J. Andrew Bagnell, “Contrasting Exploration in Parameter and Action
Space: A Zeroth-Order Optimization Perspective, ” in International Conference on Artificial Intelligenceand Statistics (AISTATS), 2019.

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5Wen Sun, Geoffrey Gordon, Byron Boots, J. Andrew Bagnell,“Dual Policy Iteration,” in Neural Information Processing Systems (NeurIPS), 2018
Ahmed Hefny, Zita Marinho, Wen Sun, Siddhartha Srinivasa, Geoffrey Gordon, “Recurrent Predictive
State Policy Networks,” in International Conference on Machine Learning (ICML), 2018
Wen Sun, J. Andrew Bagnell, Byron Boots, “Truncated Horizon Policy Search: Combining Reinforcement Learning and Imitation Learning,” in International Conference on Learning Representation (ICLR),
2018.
Huaian Diao*, Zhao Song*, Wen Sun*, David Woodruff*, “Sketching for Kronecker Product Regressionand P-splines,” in International Conference on Artificial Intelligence and Statistics (AISTATS), 2018. (Oral)
Wen Sun, Arun Venkatraman, Geoff Gordon, Byron Boots, J. Andrew Bagnell,“Deeply AggreVaTeD:Differentiable Imitation Learning for Sequential Prediction,” in International Conference on Machine
Learning (ICML), 2017.
Wen Sun, Debadeepta Dey, Ashish Kapoor, “Safety-Aware Algorithms for Adversarial Contextual Bandits,” in International Conference on Machine Learning (ICML), 2017.
Arun Venkatraman, Nicholas Rhinehart, Wen Sun, Lerrel Pinto, Martial Hebert, Byron Boots,Kris M.
Kitani, J. Andrew Bagnell, “Predictive-State Decoders: Encoding the Future into Recurrent Networks,”
in Neural Information Processing Systems (NIPS), 2017
Wen Sun, Niteesh Sood, Debadeepta Dey, Gireeja Ranade, Siddharth Prakash, Ashish Kapoor, “NoRegret Replanning Under Uncertainty,” in International Conference on Robotics and Automation (ICRA),
2017
Hanzhang Hu, Wen Sun, Arun Venkatraman, Martial Hebert, and J. Andrew Bagnell, “Online Gradient
Boosting on Stochastic Data Streams”, in International Conference on Artificial Intelligence and Statistics(AISTATS), 2017.
Wen Sun, Arun Venkatraman, Byron Boots, J. Andrew Bagnell, “Learning to Filter with Predictive State
Inference Machines,” in International Conference on Machine Learning (ICML), 2016.
Wen Sun, Roberto Capobianco, Geoffrey J. Gordon, J. Andrew Bagnell, Byron Boots, “Learning toSmooth with Bidirectional Predictive State Inference Machines,” in Uncertainty in Artificial Intelligence(UAI), 2016.
Wen Sun, J. Andrew Bagnell, “Online Bellman Residual and Temporal Difference Algorithms withPredictive Error Guarantees,” in Sister-Conference Best Paper Track, Joint Conference on Artificial Intelligence(IJCAI) 2016.
Arun Venkatraman, Wen Sun, Martial Hebert , Byron Boots, and J. Andrew Bagnell, “Inference Machines for Nonparametric Filter Learning,” in International Joint Conference on Artificial Intelligence (IJCAI), 2016.
Arun Venkatraman, Wen Sun, Martial Hebert, J. Andrew Bagnell, Byron Boots, “Online Instrumental
Variable Regression with Applications to Online Linear System Identification,” in AAAI Conference onArtificial Intelligence (AAAI), 2016.
Wen Sun, J. Andrew Bagnell, “Online Bellman Residual Algorithms with Predictive Error Guarantees,”in Uncertainty in Artificial Intelligence (UAI), 2015. (Best Student Paper Award)
Wen Sun, Islam Khalil, Sarthak Misra, Ron Alterovitz, “Motion Planning for Paramagnetic Microparticles under Motion and Sensing Uncertainty,” in International Conference on Robotics and Automation(ICRA), 2014.

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6Wen Sun, Luis Torres, Jur van den Berg, Ron Alterovitz, “Safe Motion Planning for Imprecise Robotic
Manipulators by Minimizing Probability of Collision,” in International Symposium of Robotics Research(ISRR), 2013.
(* indicates α-β order)

Funding
NSF IIS, RI: Small: Towards Provably Efficient Representation Learning in Reinforcement Learning via Rich Function Approximation ($384,616 ), 2022-2025, PI
DARPA, CASTLE: LaNCer: LeArning Network CybERagents ($3M), 2022-2026, co-PI
Cornell Infosys Collaboration: ($ 100K), 2023-2025, PI.

Teaching Experience
Instructor: CS 4780 Introduction to Machine Learning, Cornell, Fall 2022,2023.
Instructor: CS 4789 Introduction to Reinforcement Learning, Cornell, Spring 2021.
Instructor: CS 6789 Foundations of Reinforcement Learning, Cornell, Fall 2020,Fall 2021, Spring 2023.
Teaching Assistant: ROB 16831 Statistical Techniques in Robotics, CMU, Fall 2017.
Guest Lecturer: ROB 16831 Statistical Techniques in Robotics, CMU, Fall 2017 & 2018, Spring 2018.

Professional Service
Journal and Conference Article Reviewing
Area Chair, Neural Information Processing Systems (NeurIPS), 2021, 2022,2023
Area Chair, International Conference on Machine Learning (ICML), 2023
Senior Program Committee, Association for the Advancement of Artificial Intelligence, AAAI, 2020, 2021
Reviewer, Operation Research, 2021
Reviewer, Journal of Artificial Intelligence Research, 2020
Reviewer, Symposium on Foundations of Computer Science (FOCS), 2019
Reviewer, International Conference on Machine Learning (ICML), 2019, 2020
Reviewer, Neural Information Processing Systems (NeurIPS), 2016, 2018 (Ranked among top 200 reviewers), 2019,2020
Reviewer, International Conference on Artificial Intelligence and Statistics (AISTATS), 2019
Reviewer, Conference on Artificial Intelligence (AAAI), 2019
Reviewer, Asian Conference on Machine Learning (ACML), 2019
Reviewer, Conference of Robot Learning (CORL), 2018
Reviewer, Robotics: Science and Systems (RSS), 2016
Reviewer, International Conference on Intelligent Robots and Systems (IROS), 2014,2015,2016
Reviewer, International Conference on Robotics and Automation (ICRA), 2016, 2017, 2018