← Authors

Wen Sun

79 papers · 2016–2025 · 10 conferences · across top CS/AI conferences

Achievements

πŸƒ Academic Marathon (9) 🐝 Cross-Pollinator (13) 🌈 Renaissance Researcher (11) πŸ—ΊοΈ Taxonomy Completionist (12) 🧭 Keyword Pioneer 🐣 Hot Topic Early Bird πŸŒ‰ Interdisciplinary Bridge 🌍 Conference Polyglot (10) 🏠 Conference Loyalist (22) πŸ”¬ Deep Specialist (13) πŸ† Keyword Champion (4) 🀝 Dynamic Duo (12) πŸ‘‘ Triple Crown πŸ—ƒοΈ Keyword Collector (245) ⚑ Prolific Year (8) πŸš€ Conference Pioneer πŸ“ˆ Trend Setter πŸ’Ž Century Club (79) πŸ”₯ Unstoppable (10)

Conferences

NIPS (22) ICLR (21) ICML (20) AISTATS (5) COLT (3) EMNLP (2) IJCAI (2) L4DC (2) CVPR (1) INTERSPEECH (1)

Top co-authors

Masatoshi Uehara (12) Nathan Kallus (11) Yuda Song (9) Jason D. Lee (8) Wenhao Zhan (7) Ayush Sekhari (7) Kaiwen Wang (7) Byron Boots (7) Akshay Krishnamurthy (7) J. Andrew Bagnell (7)

Research topics

Reinforcement Learning (1)

Keywords

reinforcement learning (8) imitation learning (7) sample complexity (7) regret bound (6) model-based reinforcement learning (5) online learning (5) continuous control (4) linear mdp (4) policy learning (4) policy gradient (4) representation learning (4) function approximation (4) contextual bandit (3) latent state (3) policy optimization (3) linear quadratic regulator (3) offline reinforcement learning (3) off-policy evaluation (2) value iteration (2) optimal control (2)

Papers

Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds ICLR 2025
Diffusing States and Matching Scores: A New Framework for Imitation Learning ICLR 2025
A Reductions Approach to Risk-Sensitive Reinforcement Learning with Optimized Certainty Equivalents ICML 2025
Efficient Imitation under Misspecification ICLR 2025
On Speeding Up Language Model Evaluation ICLR 2025
Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics ICLR 2025
Regressing the Relative Future: Efficient Policy Optimization for Multi-turn RLHF ICLR 2025
SDGO: Self-Discrimination-Guided Optimization for Consistent Safety in Large Language Models EMNLP 2025
Convergence of Consistency Model with Multistep Sampling under General Data Assumptions ICML 2025
Correcting the Mythos of KL-Regularization: Direct Alignment without Overoptimization via Chi-Squared Preference Optimization ICLR 2025
Offline Data Enhanced On-Policy Policy Gradient with Provable Guarantees ICLR 2024
Making RL with Preference-based Feedback Efficient via Randomization ICLR 2024
Adversarial Imitation Learning via Boosting ICLR 2024
The Importance of Online Data: Understanding Preference Fine-tuning via Coverage NIPS 2024
REBEL: Reinforcement Learning via Regressing Relative Rewards NIPS 2024
Efficient and Sharp Off-Policy Evaluation in Robust Markov Decision Processes NIPS 2024
Faster Recalibration of an Online Predictor via Approachability AISTATS 2024
Provably Efficient CVaR RL in Low-rank MDPs ICLR 2024
Provable Reward-Agnostic Preference-Based Reinforcement Learning ICLR 2024
More Benefits of Being Distributional: Second-Order Bounds for Reinforcement Learning ICML 2024
Provable Offline Preference-Based Reinforcement Learning ICLR 2024
Distributional Offline Policy Evaluation with Predictive Error Guarantees ICML 2023
Hybrid RL: Using both offline and online data can make RL efficient ICLR 2023
PAC Reinforcement Learning for Predictive State Representations ICLR 2023
Multi-task Representation Learning for Pure Exploration in Linear Bandits ICML 2023
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings ICML 2023
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning NIPS 2023
Contextual Bandits and Imitation Learning with Preference-Based Active Queries NIPS 2023
Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage NIPS 2023
Reward Finetuning for Faster and More Accurate Unsupervised Object Discovery NIPS 2023
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs NIPS 2023
Selective Sampling and Imitation Learning via Online Regression NIPS 2023
Provable Benefits of Representational Transfer in Reinforcement Learning COLT 2023
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR ICML 2023
Online No-regret Model-Based Meta RL for Personalized Navigation L4DC 2022
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems NIPS 2022
Corruption-robust Offline Reinforcement Learning AISTATS 2022
Learning To Detect Mobile Objects From LiDAR Scans Without Labels CVPR 2022
Visual Named Entity Linking: A New Dataset and A Baseline EMNLP 2022
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage ICLR 2022
Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception ICLR 2022
Transform2Act: Learning a Transform-and-Control Policy for Efficient Agent Design ICLR 2022
Representation Learning for Online and Offline RL in Low-rank MDPs ICLR 2022
Learning Bellman Complete Representations for Offline Policy Evaluation ICML 2022
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning approach ICML 2022
On the Effectiveness of Iterative Learning Control L4DC 2022
Corruption-robust exploration in episodic reinforcement learning COLT 2021
MobILE: Model-Based Imitation Learning From Observation Alone NIPS 2021
Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage NIPS 2021
Bilinear Classes: A Structural Framework for Provable Generalization in RL ICML 2021
PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration ICML 2021
Fairness of Exposure in Stochastic Bandits ICML 2021
Robust Policy Gradient against Strong Data Corruption ICML 2021
Constrained episodic reinforcement learning in concave-convex and knapsack settings NIPS 2020
Provably Efficient Model-based Policy Adaptation ICML 2020
Disagreement-Regularized Imitation Learning ICLR 2020
Information Theoretic Regret Bounds for Online Nonlinear Control NIPS 2020
Learning the Linear Quadratic Regulator from Nonlinear Observations NIPS 2020
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning NIPS 2020
Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates NIPS 2020
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs NIPS 2020
Provably Efficient Imitation Learning from Observation Alone ICML 2019
Contrasting Exploration in Parameter and Action Space: A Zeroth-Order Optimization Perspective AISTATS 2019
Optimal Sketching for Kronecker Product Regression and Low Rank Approximation NIPS 2019
Model-based RL in Contextual Decision Processes: PAC bounds and Exponential Improvements over Model-free Approaches COLT 2019
Contextual Memory Trees ICML 2019
Policy Poisoning in Batch Reinforcement Learning and Control NIPS 2019
Recurrent Predictive State Policy Networks ICML 2018
Sketching for Kronecker Product Regression and P-splines AISTATS 2018
Dual Policy Iteration NIPS 2018
TRUNCATED HORIZON POLICY SEARCH: COMBINING REINFORCEMENT LEARNING & IMITATION LEARNING ICLR 2018
Predictive-State Decoders: Encoding the Future into Recurrent Networks NIPS 2017
The Frequency Range of β€œThe Ling Six Sounds” in Standard Chinese INTERSPEECH 2017
Safety-Aware Algorithms for Adversarial Contextual Bandit ICML 2017
Deeply AggreVaTeD: Differentiable Imitation Learning for Sequential Prediction ICML 2017
Gradient Boosting on Stochastic Data Streams AISTATS 2017
Inference Machines for Nonparametric Filter Learning IJCAI 2016
Online Bellman Residual and Temporal Difference Algorithms with Predictive Error Guarantees IJCAI 2016
Learning to Filter with Predictive State Inference Machines ICML 2016