5 citations · 17 across the 23 of their papers we have counts for
4 papers · 1 filter
Tight Bounds for Local Glivenko-Cantelli
Moïse Blanchard, Václav Voráček
This paper addresses the statistical problem of estimating the infinite-norm deviation from the empirical mean to the distribution mean for high-dimensional distributions on $\{0,1…
Memory-Constrained Algorithms for Convex Optimization via Recursive Cutting-Planes
Moïse Blanchard, Junhui Zhang, Patrick Jaillet
We propose a family of recursive cutting-plane algorithms to solve feasibility problems with constrained memory, which can also be used for first-order convex optimization. Precise…
Adversarial Rewards in Universal Learning for Contextual Bandits
Moise Blanchard, Steve Hanneke, Patrick Jaillet
We study the fundamental limits of learning in contextual bandits, where a learner's rewards depend on their actions and a known context, which extends the canonical multi-armed ba…
Quadratic Memory is Necessary for Optimal Query Complexity in Convex Optimization: Center-of-Mass is Pareto-Optimal
Moïse Blanchard, Junhui Zhang, Patrick Jaillet
We give query complexity lower bounds for convex optimization and the related feasibility problem. We show that quadratic memory is necessary to achieve the optimal oracle complexi…