27 citations · 65 across the 10 of their papers we have counts for
10 papers
An -No-Regret Algorithm For Graphical Bilinear Bandits
Geovani Rizk, Igor Colin, Albert Thomas +2
We propose the first regret-based approach to the Graphical Bilinear Bandits problem, where agents in a graph play a stochastic bilinear bandit game with each of their neighbor…
Refined bounds for randomized experimental design
Geovani Rizk, Igor Colin, Albert Thomas +1
Experimental design is an approach for selecting samples among a given set so as to obtain the best estimator for a given criterion. In the context of linear regression, several op…
Best Arm Identification in Graphical Bilinear Bandits
Geovani Rizk, Albert Thomas, Igor Colin +2
We introduce a new graphical bilinear bandit problem where a learner (or a \emph{central entity}) allocates arms to the nodes of a graph and observes for each edge a noisy bilinear…
Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning
Igor Colin, Ludovic Dos Santos, Kevin Scaman
We investigate the theoretical limits of pipeline parallel learning of deep learning architectures, a distributed setup in which the computation is distributed per layer instead of…
Parallel Contextual Bandits in Wireless Handover Optimization
Igor Colin, Albert Thomas, Moez Draief
As cellular networks become denser, a scalable and dynamic tuning of wireless base station parameters can only be achieved through automated optimization. Although the contextual b…
An Approximate Shapley-Folkman Theorem
Thomas Kerdreux, Igor Colin, Alexandre d'Aspremont
The Shapley-Folkman theorem shows that Minkowski averages of uniformly bounded sets tend to be convex when the number of terms in the sum becomes much larger than the ambient dimen…