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09/10/2023
Representation Learning in Low-rank Slate-based Recommender Systems
Reinforcement learning (RL) in recommendation systems offers the potenti...
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07/24/2023
Contextual Bandits and Imitation Learning via Preference-Based Active Queries
We consider the problem of contextual bandits and imitation learning, wh...
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07/21/2023
JoinGym: An Efficient Query Optimization Environment for Reinforcement Learning
In this paper, we present JoinGym, an efficient and lightweight query op...
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07/11/2023
Selective Sampling and Imitation Learning via Online Regression
We consider the problem of Imitation Learning (IL) by actively querying ...
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06/20/2023
Learning to Generate Better Than Your LLM
Reinforcement learning (RL) has emerged as a powerful paradigm for fine-...
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05/29/2023
How to Query Human Feedback Efficiently in RL?
Reinforcement Learning with Human Feedback (RLHF) is a paradigm in which...
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05/25/2023
The Benefits of Being Distributional: Small-Loss Bounds for Reinforcement Learning
While distributional reinforcement learning (RL) has demonstrated empiri...
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05/24/2023
Provable Offline Reinforcement Learning with Human Feedback
In this paper, we investigate the problem of offline reinforcement learn...
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02/19/2023
Distributional Offline Policy Evaluation with Predictive Error Guarantees
We study the problem of estimating the distribution of the return of a p...
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02/09/2023
Multi-task Representation Learning for Pure Exploration in Linear Bandits
Despite the recent success of representation learning in sequential deci...
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02/07/2023
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR
In this paper, we study risk-sensitive Reinforcement Learning (RL), focu...
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02/05/2023
Refined Value-Based Offline RL under Realizability and Partial Coverage
In offline reinforcement learning (RL) we have no opportunity to explore...
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10/13/2022
Hybrid RL: Using Both Offline and Online Data Can Make RL Efficient
We consider a hybrid reinforcement learning setting (Hybrid RL), in whic...
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07/29/2022
Sample-efficient Safe Learning for Online Nonlinear Control with Control Barrier Functions
Reinforcement Learning (RL) and continuous nonlinear control have been s...
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07/26/2022
Future-Dependent Value-Based Off-Policy Evaluation in POMDPs
We study off-policy evaluation (OPE) for partially observable MDPs (POMD...
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07/12/2022
Learning Bellman Complete Representations for Offline Policy Evaluation
We study representation learning for Offline Reinforcement Learning (RL)...
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07/12/2022
PAC Reinforcement Learning for Predictive State Representations
In this paper we study online Reinforcement Learning (RL) in partially o...
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06/24/2022
Computationally Efficient PAC RL in POMDPs with Latent Determinism and Conditional Embeddings
We study reinforcement learning with function approximation for large-sc...
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06/24/2022
Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems
We study Reinforcement Learning for partially observable dynamical syste...
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06/17/2022
Minimum Noticeable Difference based Adversarial Privacy Preserving Image Generation
Deep learning models are found to be vulnerable to adversarial examples,...
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05/29/2022
Provable Benefits of Representational Transfer in Reinforcement Learning
We study the problem of representational transfer in RL, where an agent ...
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03/29/2022
Learning to Detect Mobile Objects from LiDAR Scans Without Labels
Current 3D object detectors for autonomous driving are almost entirely t...
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03/22/2022
Hindsight is 20/20: Leveraging Past Traversals to Aid 3D Perception
Self-driving cars must detect vehicles, pedestrians, and other traffic p...
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01/31/2022
Efficient Reinforcement Learning in Block MDPs: A Model-free Representation Learning Approach
We present BRIEE (Block-structured Representation learning with Interlea...
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11/17/2021
On the Effectiveness of Iterative Learning Control
Iterative learning control (ILC) is a powerful technique for high perfor...
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10/09/2021
Representation Learning for Online and Offline RL in Low-rank MDPs
This work studies the question of Representation Learning in RL: how can...
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07/15/2021
PC-MLP: Model-based Reinforcement Learning with Policy Cover Guided Exploration
Model-based Reinforcement Learning (RL) is a popular learning paradigm d...
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07/13/2021
Pessimistic Model-based Offline RL: PAC Bounds and Posterior Sampling under Partial Coverage
We study model-based offline Reinforcement Learning with general functio...
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06/11/2021
Corruption-Robust Offline Reinforcement Learning
We study the adversarial robustness in offline reinforcement learning. G...
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06/06/2021
Mitigating Covariate Shift in Imitation Learning via Offline Data Without Great Coverage
This paper studies offline Imitation Learning (IL) where an agent learns...
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03/19/2021
Bilinear Classes: A Structural Framework for Provable Generalization in RL
This work introduces Bilinear Classes, a new structural framework, which...
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03/03/2021
Fairness of Exposure in Stochastic Bandits
Contextual bandit algorithms have become widely used for recommendation ...
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02/22/2021
Optimism is All You Need: Model-Based Imitation Learning From Observation Alone
This paper studies Imitation Learning from Observations alone (ILFO) whe...
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02/11/2021
Robust Policy Gradient against Strong Data Corruption
We study the problem of robust reinforcement learning under adversarial ...
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02/05/2021
Finite Sample Analysis of Minimax Offline Reinforcement Learning: Completeness, Fast Rates and First-Order Efficiency
We offer a theoretical characterization of off-policy evaluation (OPE) i...
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10/25/2020
Adaptive Federated Learning and Digital Twin for Industrial Internet of Things
Industrial Internet of Things (IoT) enables distributed intelligent serv...
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10/08/2020
Learning the Linear Quadratic Regulator from Nonlinear Observations
We introduce a new problem setting for continuous control called the LQR...
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07/16/2020
PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning
Direct policy gradient methods for reinforcement learning are a successf...
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06/22/2020
Information Theoretic Regret Bounds for Online Nonlinear Control
This work studies the problem of sequential control in an unknown, nonli...
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06/18/2020
FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs
In order to deal with the curse of dimensionality in reinforcement learn...
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06/14/2020
Provably Efficient Model-based Policy Adaptation
The high sample complexity of reinforcement learning challenges its use ...
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06/09/2020
Constrained episodic reinforcement learning in concave-convex and knapsack settings
We propose an algorithm for tabular episodic reinforcement learning with...
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05/27/2020
Arbitrary Style Transfer via Multi-Adaptation Network
Arbitrary style transfer is a significant topic with both research value...
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03/31/2020
Exploration in Action Space
Parameter space exploration methods with black-box optimization have rec...
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11/20/2019
Corruption Robust Exploration in Episodic Reinforcement Learning
We initiate the study of multi-stage episodic reinforcement learning und...
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10/13/2019
Policy Poisoning in Batch Reinforcement Learning and Control
We study a security threat to batch reinforcement learning and control w...
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09/29/2019
Optimal Sketching for Kronecker Product Regression and Low Rank Approximation
We study the Kronecker product regression problem, in which the design m...
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05/30/2019
Imitation Learning as f-Divergence Minimization
We address the problem of imitation learning with multi-modal demonstrat...
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05/27/2019
Provably Efficient Imitation Learning from Observation Alone
We study Imitation Learning (IL) from Observations alone (ILFO) in large...
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05/01/2019
