84 citations · 603 across the 59 of their papers we have counts for
59 papers
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…
Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient
Yuda Song, Yifei Zhou, Ayush Sekhari +3
We consider a hybrid reinforcement learning setting (Hybrid RL), in which an agent has access to an offline dataset and the ability to collect experience via real-world online inte…
PAC Reinforcement Learning for Predictive State Representations
Wenhao Zhan, Masatoshi Uehara, Wen Sun +1
In this paper we study online Reinforcement Learning (RL) in partially observable dynamical systems. We focus on the Predictive State Representations (PSRs) model, which is an expr…
Sample-efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions
Wenhao Luo, Wen Sun, Ashish Kapoor
Reinforcement Learning (RL) and continuous nonlinear control have been successfully deployed in multiple domains of complicated sequential decision-making tasks. However, given the…
Learning Bellman Complete Representations for Offline Policy Evaluation
Jonathan D. Chang, Kaiwen Wang, Nathan Kallus +1
We study representation learning for Offline Reinforcement Learning (RL), focusing on the important task of Offline Policy Evaluation (OPE). Recent work shows that, in contrast to…
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs
Masatoshi Uehara, Haruka Kiyohara, Andrew Bennett +5
We study off-policy evaluation (OPE) for partially observable MDPs (POMDPs) with general function approximation. Existing methods such as sequential importance sampling estimators…
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings
Masatoshi Uehara, Ayush Sekhari, Jason D. Lee +2
We study reinforcement learning with function approximation for large-scale Partially Observable Markov Decision Processes (POMDPs) where the state space and observation space are…
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems
Masatoshi Uehara, Ayush Sekhari, Jason D. Lee +2
We study Reinforcement Learning for partially observable dynamical systems using function approximation. We propose a new \textit{Partially Observable Bilinear Actor-Critic framewo…
Minimum Noticeable Difference based Adversarial Privacy Preserving Image Generation
Wen Sun, Jian Jin, Weisi Lin
Deep learning models are found to be vulnerable to adversarial examples, as wrong predictions can be caused by small perturbation in input for deep learning models. Most of the exi…
Provable Benefits of Representational Transfer in Reinforcement Learning
Alekh Agarwal, Yuda Song, Wen Sun +3
We study the problem of representational transfer in RL, where an agent first pretrains in a number of source tasks to discover a shared representation, which is subsequently used…
Online No-regret Model-Based Meta RL for Personalized Navigation
Yuda Song, Ye Yuan, Wen Sun +1
The interaction between a vehicle navigation system and the driver of the vehicle can be formulated as a model-based reinforcement learning problem, where the navigation systems (a…
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach
Xuezhou Zhang, Yuda Song, Masatoshi Uehara +3
We present BRIEE (Block-structured Representation learning with Interleaved Explore Exploit), an algorithm for efficient reinforcement learning in Markov Decision Processes with bl…
On the Effectiveness of Iterative Learning Control
Anirudh Vemula, Wen Sun, Maxim Likhachev +1
Iterative learning control (ILC) is a powerful technique for high performance tracking in the presence of modeling errors for optimal control applications. There is extensive prior…
Learn Zero-Constraint-Violation Policy in Model-Free Constrained Reinforcement Learning
Haitong Ma, Changliu Liu, Shengbo Eben Li +3
In the trial-and-error mechanism of reinforcement learning (RL), a notorious contradiction arises when we expect to learn a safe policy: how to learn a safe policy without enough d…
Representation Learning for Online and Offline RL in Low-rank MDPs
Masatoshi Uehara, Xuezhou Zhang, Wen Sun
This work studies the question of Representation Learning in RL: how can we learn a compact low-dimensional representation such that on top of the representation we can perform RL…