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 …

Abstract We study the Stochastic Multi-armed Bandit problem under bounded arm-memory.
In this setting, the arms arrive in a stream, and the number of arms that can be stored in the …

We study pure exploration with infinitely many bandit arms generated\iid from an unknown
distribution. Our goal is to efficiently select a single high quality arm whose average reward …

We consider the problem of ranking n experts based on their performances on d tasks. We
make a monotonicity assumption stating that for each pair of experts, one outperforms the …

We study the algorithm configuration (AC) problem, in which one seeks to find an optimal
parameter configuration of a given target algorithm in an automated way. Although this field …

We study a sequential matching problem faced by" large" centralized platforms where" jobs"
must be matched to" workers" subject to uncertainty about worker skill proficiencies. Jobs …

While deep learning has enabled significant progress in designing general purpose robot
grasping systems, there remain objects which still pose challenges for these systems …

We consider a variant of the stochastic multi-armed bandit problem where arms are known
to be organized into different groups having the same mean. The groups are unknown but a …

Stochastic Rising Bandits (SRBs) model sequential decision-making problems in which the
expected rewards of the available options increase every time they are selected. This setting …

We study pure exploration with infinitely many bandit arms generated iid from an unknown
distribution. Our goal is to efficiently select a single high quality arm whose average reward …