A brain-computer interface (BCI) is a technology that enables direct
com...
In this paper, we address the following problem: Given an offline
demons...
We investigate the extent to which offline demonstration data can improv...
The focus of this work is sample-efficient deep reinforcement learning (...
Information-directed sampling (IDS) has revealed its potential as a
data...
Information-directed sampling (IDS) has recently demonstrated its potent...
We propose an interacting contour stochastic gradient Langevin dynamics
...
Posterior predictive distributions quantify uncertainties ignored by poi...
We study query and computationally efficient planning algorithms with li...
We study a bandit version of phase retrieval where the learner chooses
a...
Stochastic sparse linear bandits offer a practical model for high-dimens...
Many reinforcement learning algorithms can be seen as versions of approx...
Bootstrapping provides a flexible and effective approach for assessing t...
Stochastic linear bandits with high-dimensional sparse features are a
pr...
This paper provides a statistical analysis of high-dimensional batch
Rei...
We investigate the hardness of online reinforcement learning in fixed
ho...
In recent years, multi-dimensional online decision making has been playi...
In this paper, we propose a novel perturbation-based exploration method ...
Model-free reinforcement learning algorithms combined with value functio...
Contextual bandits serve as a fundamental model for many sequential deci...
Upper Confidence Bound (UCB) method is arguably the most celebrated one ...
Tensors are becoming prevalent in modern applications such as medical im...
In this paper, we propose a general framework for sparse and low-rank te...