5 citations · 17 across the 23 of their papers we have counts for
7 papers · 1 filter
Bandit PCA with Minimax Optimal Regret
Moïse Blanchard, Dmitrii Ostrovskii, Aadirupa Saha
We study the bandit-feedback version of online principal component analysis (Bandit PCA): in each round , the adversary selects a symmetric gain matrix…
Distribution-Free Sequential Prediction with Abstentions
Jialin Yu, Moïse Blanchard
We study a sequential prediction problem in which an adversary is allowed to inject arbitrarily many adversarial instances in a stream of i.i.d. instances, but at each round, the l…
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…
Universal Regression with Adversarial Responses
Moïse Blanchard, Patrick Jaillet
We provide algorithms for regression with adversarial responses under large classes of non-i.i.d. instance sequences, on general separable metric spaces, with provably minimal assu…
Universal Online Learning: an Optimistically Universal Learning Rule
Moïse Blanchard
We study the subject of universal online learning with non-i.i.d. processes for bounded losses. The notion of an universally consistent learning was defined by Hanneke in an effort…
Universal Online Learning with Bounded Loss: Reduction to Binary Classification
Moïse Blanchard, Romain Cosson
We study universal consistency of non-i.i.d. processes in the context of online learning. A stochastic process is said to admit universal consistency if there exists a learner that…
The Representation Power of Neural Networks: Breaking the Curse of Dimensionality
Moise Blanchard, M. Amine Bennouna
In this paper, we analyze the number of neurons and training parameters that a neural networks needs to approximate multivariate functions of bounded second mixed derivatives -- Ko…