Classical collaborative filtering, and content-based filtering methods try to learn a static
recommendation model given training data. These approaches are far from ideal in highly …

We study a specific combinatorial pure exploration stochastic bandit problem where the
learner aims at finding the set of arms whose means are above a given threshold, up to a …

We consider the problem of\textitbest arm identification with a\textitfixed budget T, in the K-
armed stochastic bandit setting, with arms distribution defined on [0, 1]. We prove that any …

Crowdsourcing generates up to thousands of ideas per contest. The selection of best ideas
is costly because of the limited number, objectivity, and attention of experts. Using a data set …

Interactive real-time streaming applications such as audio-video conferencing, online
gaming and app streaming, place stringent requirements on the network in terms of delay …

Abstract In the Best-k-Arm problem, we are given n stochastic bandit arms, each associated
with an unknown reward distribution. We are required to identify the k arms with the largest …

We consider a sequential assortment selection problem where the user choice is given by a
multinomial logit (MNL) choice model whose parameters are unknown. In each period, the …

In the classical best arm identification (Best-$1 $-Arm) problem, we are given $ n $
stochastic bandit arms, each associated with a reward distribution with an unknown mean …

Motivated by applications to process massive datasets, we study streaming algorithms for
pure exploration in Stochastic Multi-Armed Bandits (MABs). This problem was first …

We study the best arm identification (BEST-1-ARM) problem, which is defined as follows. We
are given $ n $ stochastic bandit arms. The $ i $ th arm has a reward distribution $ D_i $ with …