4 accepted papers
We study one-sided and $\alpha$-correct sequential hypothesis testing for data generated by an ergodic Markov chain. The *null* hypothesis is that the unknown transition matrix belongs to a prescribed set $\cal P$ of stochastic matrices, and the *alternative* corresponds to a disjoint set $\cal Q$. …
We consider a multi-armed bandit setting with finitely many arms, in which each arm yields an $M$-dimensional vector reward upon selection. We assume that the reward of each dimension (a.k.a. {\em objective}) is generated independently of the others. The best arm of any given objective is the arm wi…
We study best arm identification (BAI) in linear bandits in the fixed-budget regime under differential privacy constraints, when the arm rewards are supported on the unit interval. Given a finite budget $T$ and a privacy parameter $\varepsilon>0$, the goal is to minimise the error probability in f…
We study the problem of best arm identification in a federated learning multi-armed bandit setup with a central server and multiple clients. Each client is associated with a multi-armed bandit in which each arm yields i.i.d. rewards following a Gaussian distribution with an unknown mean and known va…