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 …

Multi-armed bandit problems are the most basic examples of sequential decision problems
with an exploration-exploitation trade-off. This is the balance between staying with the option …

We develop efficient solution methods for a robust empirical risk minimization problem
designed to give calibrated confidence intervals on performance and provide optimal …

The question of the optimality of Thompson Sampling for solving the stochastic multi-armed
bandit problem had been open since 1933. In this paper we answer it positively for the case …

This paper presents a finite-time analysis of the KL-UCB algorithm, an online, horizon-free
index policy for stochastic bandit problems. We prove two distinct results: first, for arbitrary …

We consider optimal sequential allocation in the context of the so-called stochastic multi-
armed bandit model. We describe a generic index policy, in the sense of Gittins [JR Stat …

Batched bandit problems Page 1 The Annals of Statistics 2016, Vol. 44, No. 2, 660–681 DOI:
10.1214/15-AOS1381 © Institute of Mathematical Statistics, 2016 BATCHED BANDIT …

In this paper, we study the multi-armed bandit problem in the batched setting where the
employed policy must split data into a small number of batches. While the minimax regret for …

We study the problem of combining multiple bandit algorithms (that is, online learning
algorithms with partial feedback) with the goal of creating a master algorithm that performs …