19 citations · 55 across the 11 of their papers we have counts for
4 papers · 1 filter
Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settings
Rémi Bonnefoi, Lilian Besson, Christophe Moy +2
Setting up the future Internet of Things (IoT) networks will require to support more and more communicating devices. We prove that intelligent devices in unlicensed bands can use M…
Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling
Emilie Kaufmann, Wouter Koolen, Aurelien Garivier
Learning the minimum/maximum mean among a finite set of distributions is a fundamental sub-task in planning, game tree search and reinforcement learning. We formalize this learning…
What Doubling Tricks Can and Can't Do for Multi-Armed Bandits
Lilian Besson, Emilie Kaufmann
An online reinforcement learning algorithm is anytime if it does not need to know in advance the horizon T of the experiment. A well-known technique to obtain an anytime algorithm…
Pure Exploration in Infinitely-Armed Bandit Models with Fixed-Confidence
Maryam Aziz, Jesse Anderton, Emilie Kaufmann +1
We consider the problem of near-optimal arm identification in the fixed confidence setting of the infinitely armed bandit problem when nothing is known about the arm reservoir dist…