Safe Motion Planning for Imprecise Robotic Manipulators by Minimizing Probability of Collision. - Wen Sun 002; Luis G. Torres; Jur van den Berg; Ron Alterovitz (ISRR, 2013)

Motion planning for paramagnetic microparticles under motion and sensing uncertainty. - Wen Sun 002; Islam S. M. Khalil; Sarthak Misra; Ron Alterovitz (ICRA, 2014)

Motion planning under uncertainty for medical needle steering using optimization in belief space. - Wen Sun 002; Ron Alterovitz (IROS, 2014)

Online Bellman Residual Algorithms with Predictive Error Guarantees. - Wen Sun 002; J. Andrew Bagnell (UAI, 2015)

High-Frequency Replanning Under Uncertainty Using Parallel Sampling-Based Motion Planning. - Wen Sun 002; Sachin Patil; Ron Alterovitz (IEEE Trans. Robotics, 2015)

Online Instrumental Variable Regression with Applications to Online Linear System Identification. - Arun Venkatraman; Wen Sun 002; Martial Hebert; J. Andrew Bagnell; Byron Boots (AAAI, 2016)

Learning to Filter with Predictive State Inference Machines. - Wen Sun 002; Arun Venkatraman; Byron Boots; J. Andrew Bagnell (ICML, 2016)

Online Bellman Residual and Temporal Difference Algorithms with Predictive Error Guarantees. - Wen Sun 002; J. Andrew Bagnell (IJCAI, 2016)

Inference Machines for Nonparametric Filter Learning. - Arun Venkatraman; Wen Sun 002; Martial Hebert; Byron Boots; J. Andrew Bagnell (IJCAI, 2016)

Learning to Smooth with Bidirectional Predictive State Inference Machines. - Wen Sun 002; Roberto Capobianco; Geoffrey J. Gordon; J. Andrew Bagnell; Byron Boots (UAI, 2016)

Stochastic Extended LQR for Optimization-Based Motion Planning Under Uncertainty. - Wen Sun 002; Jur van den Berg; Ron Alterovitz (IEEE Trans Autom. Sci. Eng., 2016)

Gradient Boosting on Stochastic Data Streams. - Hanzhang Hu; Wen Sun 002; Arun Venkatraman; Martial Hebert; J. Andrew Bagnell (AISTATS, 2017)

Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction. - Wen Sun 002; Arun Venkatraman; Geoffrey J. Gordon; Byron Boots; J. Andrew Bagnell (ICML, 2017)

Predictive-State Decoders: Encoding the Future into Recurrent Networks. - Arun Venkatraman; Nicholas Rhinehart; Wen Sun 002; Lerrel Pinto; Martial Hebert; Byron Boots; Kris M. Kitani; James Andrew Bagnell (NIPS, 2017)

Truncated horizon Policy Search: Combining Reinforcement Learning & Imitation Learning. - Wen Sun 002; J. Andrew Bagnell; Byron Boots (ICLR (Poster), 2018)

Recurrent Predictive State Policy Networks. - Ahmed Hefny; Zita Marinho; Wen Sun 002; Siddhartha S. Srinivasa; Geoffrey J. Gordon (ICML, 2018)

Dual Policy Iteration. - Wen Sun 002; Geoffrey J. Gordon; Byron Boots; J. Andrew Bagnell (NeurIPS, 2018)

Model-Based Reinforcement Learning in Contextual Decision Processes. - Wen Sun 002; Nan Jiang 008; Akshay Krishnamurthy; Alekh Agarwal; John Langford 001 (CoRR, 2018)

Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective. - Anirudh Vemula; Wen Sun 002; J. Andrew Bagnell (AISTATS, 2019)

Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches. - Wen Sun 002; Nan Jiang 008; Akshay Krishnamurthy; Alekh Agarwal; John Langford 001 (COLT, 2019)

Provably Efficient Imitation Learning from Observation Alone. - Wen Sun 002; Anirudh Vemula; Byron Boots; Drew Bagnell (ICML, 2019)

Contextual Memory Trees. - Wen Sun 002; Alina Beygelzimer; Hal Daumé III; John Langford 001; Paul Mineiro (ICML, 2019)

Policy Poisoning in Batch Reinforcement Learning and Control. - Yuzhe Ma; Xuezhou Zhang; Wen Sun 002; Jerry Zhu (NeurIPS, 2019)

Policy Poisoning in Batch Reinforcement Learning and Control. - Yuzhe Ma; Xuezhou Zhang; Wen Sun 002; Xiaojin Zhu 001 (CoRR, 2019)

Towards Generalization and Efficiency in Reinforcement Learning. - Wen Sun 002 (2019)

Disagreement-Regularized Imitation Learning. - Kianté Brantley; Wen Sun 002; Mikael Henaff (ICLR, 2020)

Provably Efficient Model-based Policy Adaptation. - Yuda Song 001; Aditi Mavalankar; Wen Sun 002; Sicun Gao (ICML, 2020)

PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning. - Alekh Agarwal; Mikael Henaff; Sham M. Kakade; Wen Sun 002 (NeurIPS, 2020)

FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs. - Alekh Agarwal; Sham M. Kakade; Akshay Krishnamurthy; Wen Sun 002 (NeurIPS, 2020)

Constrained episodic reinforcement learning in concave-convex and knapsack settings. - Kianté Brantley; Miroslav Dudík; Thodoris Lykouris; Sobhan Miryoosefi; Max Simchowitz; Aleksandrs Slivkins; Wen Sun 002 (NeurIPS, 2020)

Information Theoretic Regret Bounds for Online Nonlinear Control. - Sham M. Kakade; Akshay Krishnamurthy; Kendall Lowrey; Motoya Ohnishi; Wen Sun 002 (NeurIPS, 2020)

Learning the Linear Quadratic Regulator from Nonlinear Observations. - Zakaria Mhammedi; Dylan J. Foster; Max Simchowitz; Dipendra Misra; Wen Sun 002; Akshay Krishnamurthy; Alexander Rakhlin; John Langford 001 (NeurIPS, 2020)

Exploration in Action Space. - Anirudh Vemula; Wen Sun 002; J. Andrew Bagnell (CoRR, 2020)

Bilinear Classes: A Structural Framework for Provable Generalization in RL. - Simon S. Du; Sham M. Kakade; Jason D. Lee; Shachar Lovett; Gaurav Mahajan; Wen Sun 002; Ruosong Wang (ICML, 2021)

PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration. - Yuda Song 001; Wen Sun 002 (ICML, 2021)

Fairness of Exposure in Stochastic Bandits. - Lequn Wang; Yiwei Bai; Wen Sun 002; Thorsten Joachims (ICML, 2021)

Robust Policy Gradient against Strong Data Corruption. - Xuezhou Zhang; Yiding Chen; Xiaojin Zhu 001; Wen Sun 002 (ICML, 2021)

Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage. - Jonathan D. Chang; Masatoshi Uehara; Dhruv Sreenivas; Rahul Kidambi; Wen Sun 002 (NeurIPS, 2021)

MobILE: Model-Based Imitation Learning From Observation Alone. - Rahul Kidambi; Jonathan D. Chang; Wen Sun 002 (NeurIPS, 2021)

Imitation Learning as f-Divergence Minimization. - Liyiming Ke; Sanjiban Choudhury; Matt Barnes 001; Wen Sun 002; Gilwoo Lee; Siddhartha S. Srinivasa (WAFR, 2021)

Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency. - Masatoshi Uehara; Masaaki Imaizumi; Nan Jiang 008; Nathan Kallus; Wen Sun 002; Tengyang Xie (CoRR, 2021)

Optimism is All You Need: Model-Based Imitation Learning From Observation Alone. - Rahul Kidambi; Jonathan D. Chang; Wen Sun 002 (CoRR, 2021)

Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage. - Jonathan D. Chang; Masatoshi Uehara; Dhruv Sreenivas; Rahul Kidambi; Wen Sun 002 (CoRR, 2021)

Corruption-Robust Offline Reinforcement Learning. - Xuezhou Zhang; Yiding Chen; Jerry Zhu; Wen Sun 002 (CoRR, 2021)

Pessimistic Model-based Offline RL: PAC Bounds and Posterior Sampling under Partial Coverage. - Masatoshi Uehara; Wen Sun 002 (CoRR, 2021)

Corruption-robust Offline Reinforcement Learning. - Xuezhou Zhang; Yiding Chen; Xiaojin Zhu 001; Wen Sun 002 (AISTATS, 2022)

Learning to Detect Mobile Objects from LiDAR Scans Without Labels. - Yurong You; Katie Luo; Cheng Perng Phoo; Wei-Lun Chao; Wen Sun 002; Bharath Hariharan; Mark E. Campbell; Kilian Q. Weinberger (CVPR, 2022)

Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design. - Ye Yuan 007; Yuda Song 001; Zhengyi Luo 002; Wen Sun 002; Kris M. Kitani (ICLR, 2022)

Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage. - Masatoshi Uehara; Wen Sun 002 (ICLR, 2022)

Representation Learning for Online and Offline RL in Low-rank MDPs. - Masatoshi Uehara; Xuezhou Zhang; Wen Sun 002 (ICLR, 2022)

Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception. - Yurong You; Katie Z. Luo; Xiangyu Chen 007; Junan Chen; Wei-Lun Chao; Wen Sun 002; Bharath Hariharan; Mark E. Campbell; Kilian Q. Weinberger (ICLR, 2022)

Learning Bellman Complete Representations for Offline Policy Evaluation. - Jonathan D. Chang; Kaiwen Wang; Nathan Kallus; Wen Sun 002 (ICML, 2022)

Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach. - Xuezhou Zhang; Yuda Song 001; Masatoshi Uehara; Mengdi Wang; Alekh Agarwal; Wen Sun 002 (ICML, 2022)

Online No-regret Model-Based Meta RL for Personalized Navigation. - Yuda Song 001; Ye Yuan 007; Wen Sun 002; Kris Kitani (L4DC, 2022)

On the Effectiveness of Iterative Learning Control. - Anirudh Vemula; Wen Sun 002; Maxim Likhachev; J. Andrew Bagnell (L4DC, 2022)

Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems. - Masatoshi Uehara; Ayush Sekhari; Jason D. Lee; Nathan Kallus; Wen Sun 002 (NeurIPS, 2022)

Provable Benefits of Representational Transfer in Reinforcement Learning. - Alekh Agarwal; Yuda Song 001; Wen Sun 002; Kaiwen Wang; Mengdi Wang; Xuezhou Zhang (COLT, 2023)

Hybrid RL: Using both offline and online data can make RL efficient. - Yuda Song 001; Yifei Zhou; Ayush Sekhari; Drew Bagnell; Akshay Krishnamurthy; Wen Sun 002 (ICLR, 2023)

PAC Reinforcement Learning for Predictive State Representations. - Wenhao Zhan; Masatoshi Uehara; Wen Sun 002; Jason D. Lee (ICLR, 2023)

Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings. - Masatoshi Uehara; Ayush Sekhari; Jason D. Lee; Nathan Kallus; Wen Sun 002 (ICML, 2023)

Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR. - Kaiwen Wang; Nathan Kallus; Wen Sun 002 (ICML, 2023)

Distributional Offline Policy Evaluation with Predictive Error Guarantees. - Runzhe Wu; Masatoshi Uehara; Wen Sun 002 (ICML, 2023)

Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery. - Katie Luo; Zhenzhen Liu; Xiangyu Chen 007; Yurong You; Sagie Benaim; Cheng Perng Phoo; Mark E. Campbell; Wen Sun 002; Bharath Hariharan; Kilian Q. Weinberger (NeurIPS, 2023)

Contextual Bandits and Imitation Learning with Preference-Based Active Queries. - Ayush Sekhari; Karthik Sridharan; Wen Sun 002; Runzhe Wu (NeurIPS, 2023)

Selective Sampling and Imitation Learning via Online Regression. - Ayush Sekhari; Karthik Sridharan; Wen Sun 002; Runzhe Wu (NeurIPS, 2023)

Future-Dependent Value-Based Off-Policy Evaluation in POMDPs. - Masatoshi Uehara; Haruka Kiyohara; Andrew Bennett; Victor Chernozhukov; Nan Jiang 008; Nathan Kallus; Chengchun Shi; Wen Sun 002 (NeurIPS, 2023)

Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage. - Masatoshi Uehara; Nathan Kallus; Jason D. Lee; Wen Sun 002 (NeurIPS, 2023)

The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning. - Kaiwen Wang; Kevin Zhou; Runzhe Wu; Nathan Kallus; Wen Sun 002 (NeurIPS, 2023)

Refined Value-Based Offline RL under Realizability and Partial Coverage. - Masatoshi Uehara; Nathan Kallus; Jason D. Lee; Wen Sun 002 (CoRR, 2023)

Provable Offline Reinforcement Learning with Human Feedback. - Wenhao Zhan; Masatoshi Uehara; Nathan Kallus; Jason D. Lee; Wen Sun 002 (CoRR, 2023)

How to Query Human Feedback Efficiently in RL? - Wenhao Zhan; Masatoshi Uehara; Wen Sun 002; Jason D. Lee (CoRR, 2023)

Learning to Generate Better Than Your LLM. - Jonathan D. Chang; Kianté Brantley; Rajkumar Ramamurthy; Dipendra Misra; Wen Sun 002 (CoRR, 2023)

JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning. - Kaiwen Wang; Junxiong Wang; Yueying Li; Nathan Kallus; Immanuel Trummer; Wen Sun 002 (CoRR, 2023)

Contextual Bandits and Imitation Learning via Preference-Based Active Queries. - Ayush Sekhari; Karthik Sridharan; Wen Sun 002; Runzhe Wu (CoRR, 2023)

Provably Efficient CVaR RL in Low-rank MDPs. - Yulai Zhao 002; Wenhao Zhan; Xiaoyan Hu; Ho-fung Leung; Farzan Farnia; Wen Sun 002; Jason D. Lee (ICLR, 2024)

Adversarial Imitation Learning via Boosting. - Jonathan D. Chang; Dhruv Sreenivas; Yingbing Huang; Kianté Brantley; Wen Sun 002 (ICLR, 2024)

Making RL with Preference-based Feedback Efficient via Randomization. - Runzhe Wu; Wen Sun 002 (ICLR, 2024)

Provable Reward-Agnostic Preference-Based Reinforcement Learning. - Wenhao Zhan; Masatoshi Uehara; Wen Sun 002; Jason D. Lee (ICLR, 2024)

Provable Offline Preference-Based Reinforcement Learning. - Wenhao Zhan; Masatoshi Uehara; Nathan Kallus; Jason D. Lee; Wen Sun 002 (ICLR, 2024)

Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees. - Yifei Zhou; Ayush Sekhari; Yuda Song 001; Wen Sun 002 (ICLR, 2024)

More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning. - Kaiwen Wang; Owen Oertell; Alekh Agarwal; Nathan Kallus; Wen Sun 002 (ICML, 2024)

The Importance of Online Data: Understanding Preference Fine-tuning via Coverage. - Yuda Song 001; Gokul Swamy; Aarti Singh; J. Andrew Bagnell; Wen Sun 002 (NeurIPS, 2024)

Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes. - Andrew Bennett; Nathan Kallus; Miruna Oprescu; Wen Sun 002; Kaiwen Wang (NeurIPS, 2024)

REBEL: Reinforcement Learning via Regressing Relative Rewards. - Zhaolin Gao; Jonathan D. Chang; Wenhao Zhan; Owen Oertell; Gokul Swamy; Kianté Brantley; Thorsten Joachims; Drew Bagnell; Jason D. Lee; Wen Sun 002 (NeurIPS, 2024)

RL for Consistency Models: Reward Guided Text-to-Image Generation with Fast Inference. - Owen Oertell; Jonathan D. Chang; Yiyi Zhang; Kianté Brantley; Wen Sun 002 (RLJ, 2024)

JoinGym: An Efficient Join Order Selection Environment. - Junxiong Wang; Kaiwen Wang; Yueying Li; Nathan Kallus; Immanuel Trummer; Wen Sun 002 (RLJ, 2024)

Risk-Sensitive RL with Optimized Certainty Equivalents via Reduction to Standard RL. - Kaiwen Wang; Dawen Liang; Nathan Kallus; Wen Sun 002 (CoRR, 2024)

RL for Consistency Models: Faster Reward Guided Text-to-Image Generation. - Owen Oertell; Jonathan D. Chang; Yiyi Zhang; Kianté Brantley; Wen Sun 002 (CoRR, 2024)

Dataset Reset Policy Optimization for RLHF. - Jonathan D. Chang; Wenhao Zhan; Owen Oertell; Kianté Brantley; Dipendra Misra; Jason D. Lee; Wen Sun 002 (CoRR, 2024)

Understanding Preference Fine-Tuning Through the Lens of Coverage. - Yuda Song 001; Gokul Swamy; Aarti Singh; J. Andrew Bagnell; Wen Sun 002 (CoRR, 2024)

Orchestrating LLMs with Different Personalizations. - Jin Peng Zhou; Katie Z. Luo; Jingwen Gu; Jason Yuan; Kilian Q. Weinberger; Wen Sun 002 (CoRR, 2024)

The Central Role of the Loss Function in Reinforcement Learning. - Kaiwen Wang; Nathan Kallus; Wen Sun 002 (CoRR, 2024)

Efficient Imitation under Misspecification. - Nicolas A. Espinosa Dice; Sanjiban Choudhury; Wen Sun 002; Gokul Swamy (ICLR, 2025)

Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHF. - Zhaolin Gao; Wenhao Zhan; Jonathan Daniel Chang; Gokul Swamy; Kianté Brantley; Jason D. Lee; Wen Sun 002 (ICLR, 2025)

Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization. - Audrey Huang; Wenhao Zhan; Tengyang Xie; Jason D. Lee; Wen Sun 002; Akshay Krishnamurthy; Dylan J. Foster (ICLR, 2025)

Diffusing States and Matching Scores: A New Framework for Imitation Learning. - Runzhe Wu; Yiding Chen; Gokul Swamy; Kianté Brantley; Wen Sun 002 (ICLR, 2025)

Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics. - Runzhe Wu; Ayush Sekhari; Akshay Krishnamurthy; Wen Sun 002 (ICLR, 2025)

On Speeding Up Language Model Evaluation. - Jin Peng Zhou; Christian K. Belardi; Ruihan Wu; Travis Zhang; Carla P. Gomes; Wen Sun 002; Kilian Q. Weinberger (ICLR, 2025)

Corruption-Robust Exploration in Episodic Reinforcement Learning. - Thodoris Lykouris; Max Simchowitz; Aleksandrs Slivkins; Wen Sun 002 (Math. Oper. Res., 2025)

Q♯: Provably Optimal Distributional RL for LLM Post-Training. - Jin Peng Zhou; Kaiwen Wang; Jonathan D. Chang; Zhaolin Gao; Nathan Kallus; Kilian Q. Weinberger; Kianté Brantley; Wen Sun 002 (CoRR, 2025)

All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning. - Gokul Swamy; Sanjiban Choudhury; Wen Sun 002; Zhiwei Steven Wu; J. Andrew Bagnell (CoRR, 2025)

Value-Guided Search for Efficient Chain-of-Thought Reasoning. - Kaiwen Wang; Jin Peng Zhou; Jonathan D. Chang; Zhaolin Gao; Nathan Kallus; Kianté Brantley; Wen Sun 002 (CoRR, 2025)

Accelerating RL for LLM Reasoning with Optimal Advantage Regression. - Kianté Brantley; Mingyu Chen; Zhaolin Gao; Jason D. Lee; Wen Sun 002; Wenhao Zhan; Xuezhou Zhang (CoRR, 2025)

Scaling Offline RL via Efficient and Expressive Shortcut Models. - Nicolas A. Espinosa Dice; Yiyi Zhang; Yiding Chen; Bradley Guo; Owen Oertell; Gokul Swamy; Kianté Brantley; Wen Sun 002 (CoRR, 2025)