The Densest Subgraph Problem requires us to find, in a given graph, a subset of vertices
whose induced subgraph maximizes a measure of density. The problem has received a …

Decision-making in the face of uncertainty is a significant challenge in machine learning,
and the multi-armed bandit model is a commonly used framework to address it. This …

In this paper, we address the important issue of uncertainty in the edge influence probability
estimates for the well studied influence maximization problem---the task of finding k seed …

We investigate an active\emph {pure-exploration} setting, that includes\emph {best-arm
identification}, in the context of\emph {linear stochastic bandits}. While asymptotically optimal …

In this paper we introduce the pure exploration transductive linear bandit problem: given a
set of measurement vectors $\mathcal {X}\subset\mathbb {R}^ d $, a set of items $\mathcal …

This paper presents new deviation inequalities that are valid uniformly in time under
adaptive sampling in a multi-armed bandit model. The deviations are measured using the …

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 …

In this paper, we study the stochastic combinatorial multi-armed bandit (CMAB) framework
that allows a general nonlinear reward function, whose expected value may not depend only …

We propose the contextual combinatorial cascading bandits, a combinatorial online learning
game, where at each time step a learning agent is given a set of contextual information, then …