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 …

This paper considers the use of a simple posterior sampling algorithm to balance between
exploration and exploitation when learning to optimize actions such as in multiarmed bandit …

Thompson Sampling (TS) is one of the oldest heuristics for multiarmed bandit problems. It is
a randomized algorithm based on Bayesian ideas and has recently generated significant …

This paper considers the optimal adaptive allocation of measurement effort for identifying the
best among a finite set of options or designs. An experimenter sequentially chooses designs …

We provide an information-theoretic analysis of Thompson sampling that applies across a
broad range of online optimization problems in which a decision-maker must learn from …

We derive an alternative proof for the regret of Thompson sampling (TS) in the stochastic
linear bandit setting. While we obtain a regret bound of order $ O (d^ 3/2\sqrtT) $ as in …

This work covers several aspects of the optimism in the face of uncertainty principle applied
to large scale optimization problems under finite numerical budget. The initial motivation for …

We consider stochastic multi-armed bandit problems with complex actions over a set of
basic arms, where the decision maker plays a complex action rather than a basic arm in …

We discuss a multiple-play multi-armed bandit (MAB) problem in which several arms are
selected at each round. Recently, Thompson sampling (TS), a randomized algorithm with a …

Stochastic multi-armed bandits form a class of online learning problems that have important
applications in online recommendation systems, adaptive medical treatment, and many …