A fundamental challenge in interactive learning and decision making, ranging from bandit
problems to reinforcement learning, is to provide sample-efficient, adaptive learning …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

Multi-armed bandits a simple but very powerful framework for algorithms that make
decisions over time under uncertainty. An enormous body of work has accumulated over the …

We study the problem of best-arm identification with fixed confidence in stochastic linear
bandits. The objective is to identify the best arm with a given level of certainty while …

This paper presents new deviation inequalities that are valid uniformly in time under
adaptive sampling in a multi-armed bandit model. The deviations are measured using the …

Stochastic linear bandits with high-dimensional sparse features are a practical model for a
variety of domains, such as personalized medicine and online advertising. We derive a …

We study the problem of active pure exploration with fixed confidence in generic stochastic
bandit environments. The goal of the learner is to answer a query about the environment …

In this work, we develop linear bandit algorithms that automatically adapt to different
environments. By plugging a novel loss estimator into the optimization problem that …

Structural causal bandit provides a framework for online decision-making problems when
causal information is available. It models the stochastic environment with a structural causal …

We consider the problem of identifying the best arm in stochastic Multi-Armed Bandits
(MABs) using a fixed sampling budget. Characterizing the minimal instance-specific error …