Given a set of arms $\mathcal {Z}\subset\mathbb {R}^ d $ and an unknown parameter vector
$\theta_\ast\in\mathbb {R}^ d $, the pure exploration linear bandits problem aims to return …

We consider the best-k-arm identification problem for multi-armed bandits, where the
objective is to select the exact set of k arms with the highest mean rewards by sequentially …

Stochastic multi-armed bandits are a sequential-decision-making framework, where, at each
interaction step, the learner selects an arm and observes a stochastic reward. Within the …

We consider pure-exploration problems in the context of stochastic sequential adaptive
experiments with a finite set of alternative options. The goal of the decision-maker is to …

We investigate the Active Clustering Problem (ACP). A learner interacts with an $ N $-armed
stochastic bandit with $ d $-dimensional subGaussian feedback. There exists a hidden …

In good arm identification (GAI), the goal is to identify one arm whose average performance
exceeds a given threshold, referred to as good arm, if it exists. Few works have studied GAI …