activity
20202026
most citedBandits with many optimal arms

2 citations · 4 across the 5 of their papers we have counts for

collaborators

5 papers

stat.ML2026

Active Seriation: Efficient Ordering Recovery with Statistical Guarantees

James Cheshire, Yann Issartel

Active seriation aims at recovering an unknown ordering of items by adaptively querying pairwise similarities. The observations are noisy measurements of entries of an underlyi…

stat.ML2026

Active Bipartite Ranking with Smooth Posterior Distributions

James Cheshire, Stephan Clémençon

In this article, bipartite ranking, a statistical learning problem involved in many applications and widely studied in the passive context, is approached in a much more general \te…

stat.ML2021★ 1 cited

Problem Dependent View on Structured Thresholding Bandit Problems

James Cheshire, Pierre Ménard, Alexandra Carpentier

We investigate the problem dependent regime in the stochastic Thresholding Bandit problem (TBP) under several shape constraints. In the TBP, the objective of the learner is to outp…

cs.LG2021★ 2 cited

Bandits with many optimal arms

Rianne de Heide, James Cheshire, Pierre Ménard +1

We consider a stochastic bandit problem with a possibly infinite number of arms. We write for the proportion of optimal arms and for the minimal mean-gap between optimal…

cs.LG2020★ 1 cited

The Influence of Shape Constraints on the Thresholding Bandit Problem

James Cheshire, Pierre Menard, Alexandra Carpentier

We investigate the stochastic Thresholding Bandit problem (TBP) under several shape constraints. On top of (i) the vanilla, unstructured TBP, we consider the case where (ii) the se…