We design and analyse variations of the classical Thompson sampling (TS) procedure for
Bayesian optimisation (BO) in settings where function evaluations are expensive but can be …

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 …

As bandit algorithms are increasingly utilized in scientific studies and industrial applications,
there is an associated increasing need for reliable inference methods based on the resulting …

Motivated by practical needs such as large-scale learning, we study the impact of adaptivity
constraints to linear contextual bandits, a central problem in online learning and decision …

In many learning settings, active/adaptive querying is possible, but the number of rounds of
adaptivity is limited. We study the relationship between query complexity and adaptivity in …

Online advertising and product recommendation are important domains of applications for
multi-armed bandit methods. In these fields, the reward that is immediately available is most …

We present simple algorithms for batched stochastic multi-armed bandit and batched
stochastic linear bandit problems. We prove bounds for their expected regrets that improve …

Simple regret is a natural and parameter-free performance criterion for pure exploration in
multi-armed bandits yet is less popular than the probability of missing the best arm or an …

In this paper, we propose a generalization of the best arm identification problem in
stochastic multi-armed bandits (MAB) to the setting where every pull of an arm is associated …

In this paper we revisit the fixed-confidence identification of the Pareto optimal set in a multi-
objective multi-armed bandit model. As the sample complexity to identify the exact Pareto set …