84 citations · 603 across the 59 of their papers we have counts for
10 papers · 1 filter
Selective Sampling and Imitation Learning via Online Regression
Ayush Sekhari, Karthik Sridharan, Wen Sun +1
We consider the problem of Imitation Learning (IL) by actively querying noisy expert for feedback. While imitation learning has been empirically successful, much of prior work assu…
JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning
Kaiwen Wang, Junxiong Wang, Yueying Li +3
Join order selection (JOS) is the problem of ordering join operations to minimize total query execution cost and it is the core NP-hard combinatorial optimization problem of query…
Learning to Generate Better Than Your LLM
Jonathan D. Chang, Kiante Brantley, Rajkumar Ramamurthy +2
Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning Large Language Models (LLMs) for text generation. In particular, recent LLMs such as ChatGPT and GPT-…
Provable Reward-Agnostic Preference-Based Reinforcement Learning
Wenhao Zhan, Masatoshi Uehara, Wen Sun +1
Preference-based Reinforcement Learning (PbRL) is a paradigm in which an RL agent learns to optimize a task using pair-wise preference-based feedback over trajectories, rather than…
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning
Kaiwen Wang, Kevin Zhou, Runzhe Wu +2
While distributional reinforcement learning (DistRL) has been empirically effective, the question of when and why it is better than vanilla, non-distributional RL has remained unan…
Provable Offline Preference-Based Reinforcement Learning
Wenhao Zhan, Masatoshi Uehara, Nathan Kallus +2
In this paper, we investigate the problem of offline Preference-based Reinforcement Learning (PbRL) with human feedback where feedback is available in the form of preference betwee…
Distributional Offline Policy Evaluation with Predictive Error Guarantees
Runzhe Wu, Masatoshi Uehara, Wen Sun
We study the problem of estimating the distribution of the return of a policy using an offline dataset that is not generated from the policy, i.e., distributional offline policy ev…
Multi-task Representation Learning for Pure Exploration in Linear Bandits
Yihan Du, Longbo Huang, Wen Sun
Despite the recent success of representation learning in sequential decision making, the study of the pure exploration scenario (i.e., identify the best option and minimize the sam…
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR
Kaiwen Wang, Nathan Kallus, Wen Sun
In this paper, we study risk-sensitive Reinforcement Learning (RL), focusing on the objective of Conditional Value at Risk (CVaR) with risk tolerance . Starting with multi-arm b…
Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage
Masatoshi Uehara, Nathan Kallus, Jason D. Lee +1
In offline reinforcement learning (RL) we have no opportunity to explore so we must make assumptions that the data is sufficient to guide picking a good policy, taking the form of…