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05/17/2023
Sequential Best-Arm Identification with Application to Brain-Computer Interface
A brain-computer interface (BCI) is a technology that enables direct com...
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03/20/2023
Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale
In this paper, we address the following problem: Given an offline demons...
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02/07/2023
Leveraging Demonstrations to Improve Online Learning: Quality Matters
We investigate the extent to which offline demonstration data can improv...
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01/29/2023
Sample Efficient Deep Reinforcement Learning via Local Planning
The focus of this work is sample-efficient deep reinforcement learning (...
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06/09/2022
Regret Bounds for Information-Directed Reinforcement Learning
Information-directed sampling (IDS) has revealed its potential as a data...
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05/22/2022
Contextual Information-Directed Sampling
Information-directed sampling (IDS) has recently demonstrated its potent...
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02/20/2022
Interacting Contour Stochastic Gradient Langevin Dynamics
We propose an interacting contour stochastic gradient Langevin dynamics ...
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10/09/2021
Evaluating Predictive Distributions: Does Bayesian Deep Learning Work?
Posterior predictive distributions quantify uncertainties ignored by poi...
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08/12/2021
Efficient Local Planning with Linear Function Approximation
We study query and computationally efficient planning algorithms with li...
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06/03/2021
Bandit Phase Retrieval
We study a bandit version of phase retrieval where the learner chooses a...
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05/29/2021
Information Directed Sampling for Sparse Linear Bandits
Stochastic sparse linear bandits offer a practical model for high-dimens...
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02/11/2021
Optimization Issues in KL-Constrained Approximate Policy Iteration
Many reinforcement learning algorithms can be seen as versions of approx...
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02/06/2021
Bootstrapping Statistical Inference for Off-Policy Evaluation
Bootstrapping provides a flexible and effective approach for assessing t...
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11/08/2020
High-Dimensional Sparse Linear Bandits
Stochastic linear bandits with high-dimensional sparse features are a pr...
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11/08/2020
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient
This paper provides a statistical analysis of high-dimensional batch Rei...
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11/08/2020
Online Sparse Reinforcement Learning
We investigate the hardness of online reinforcement learning in fixed ho...
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07/31/2020
Low-rank Tensor Bandits
In recent years, multi-dimensional online decision making has been playi...
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02/19/2020
Residual Bootstrap Exploration for Bandit Algorithms
In this paper, we propose a novel perturbation-based exploration method ...
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02/08/2020
Provably Efficient Adaptive Approximate Policy Iteration
Model-free reinforcement learning algorithms combined with value functio...
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10/15/2019
Adaptive Exploration in Linear Contextual Bandit
Contextual bandits serve as a fundamental model for many sequential deci...
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06/12/2019
Bootstrapping Upper Confidence Bound
Upper Confidence Bound (UCB) method is arguably the most celebrated one ...
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03/31/2019
Sparse Tensor Additive Regression
Tensors are becoming prevalent in modern applications such as medical im...
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01/29/2018
