172 citations · 763 across the 43 of their papers we have counts for
16 papers · 1 filter
Label Shift Quantification with Robustness Guarantees via Distribution Feature Matching
Bastien Dussap, Gilles Blanchard, Badr-Eddine Chérief-Abdellatif
Quantification learning deals with the task of estimating the target label distribution under label shift. In this paper, we first present a unifying framework, distribution featur…
Covariance Adaptive Best Arm Identification
El Mehdi Saad, Gilles Blanchard, Nicolas Verzelen
We consider the problem of best arm identification in the multi-armed bandit model, under fixed confidence. Given a confidence input , the goal is to identify the arm with the h…
High-Dimensional Multi-Task Averaging and Application to Kernel Mean Embedding
Hannah Marienwald, Jean-Baptiste Fermanian, Gilles Blanchard
We propose an improved estimator for the multi-task averaging problem, whose goal is the joint estimation of the means of multiple distributions using separate, independent data se…
Online Orthogonal Matching Pursuit
El Mehdi Saad, Gilles Blanchard, Sylvain Arlot
Greedy algorithms for feature selection are widely used for recovering sparse high-dimensional vectors in linear models. In classical procedures, the main emphasis was put on the s…
Restless dependent bandits with fading memory
Oleksandr Zadorozhnyi, Gilles Blanchard, Alexandra Carpentier
We study the stochastic multi-armed bandit problem in the case when the arm samples are dependent over time and generated from so-called weak $\cC$-mixing processes. We establish a…
A minimax near-optimal algorithm for adaptive rejection sampling
Juliette Achdou, Joseph C. Lam, Alexandra Carpentier +1
Rejection Sampling is a fundamental Monte-Carlo method. It is used to sample from distributions admitting a probability density function which can be evaluated exactly at any given…
Concentration of weakly dependent Banach-valued sums and applications to statistical learning methods
Gilles Blanchard, Oleksandr Zadorozhnyi
We obtain a Bernstein-type inequality for sums of Banach-valued random variables satisfying a weak dependence assumption of general type and under certain smoothness assumptions of…
Domain Generalization by Marginal Transfer Learning
Gilles Blanchard, Aniket Anand Deshmukh, Urun Dogan +2
In the problem of domain generalization (DG), there are labeled training data sets from several related prediction problems, and the goal is to make accurate predictions on future…
Decontamination of Mutual Contamination Models
Julian Katz-Samuels, Gilles Blanchard, Clayton Scott
Many machine learning problems can be characterized by mutual contamination models. In these problems, one observes several random samples from different convex combinations of a s…
Compressive Statistical Learning with Random Feature Moments
Rémi Gribonval, Gilles Blanchard, Nicolas Keriven +1
We describe a general framework -- compressive statistical learning -- for resource-efficient large-scale learning: the training collection is compressed in one pass into a low-dim…
Kernel regression, minimax rates and effective dimensionality: beyond the regular case
Gilles Blanchard, Nicole Mücke
We investigate if kernel regularization methods can achieve minimax convergence rates over a source condition regularity assumption for the target function. These questions have be…
Optimal Rates For Regularization Of Statistical Inverse Learning Problems
Gilles Blanchard, Nicole Mücke
We consider a statistical inverse learning problem, where we observe the image of a function through a linear operator at i.i.d. random design points , superposed with…
Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning
Ilya Tolstikhin, Nikita Zhivotovskiy, Gilles Blanchard
Transductive learning considers situations when a learner observes labelled training points and unlabelled test points with the final goal of giving correct answers for the…
Localized Complexities for Transductive Learning
Ilya Tolstikhin, Gilles Blanchard, Marius Kloft
We show two novel concentration inequalities for suprema of empirical processes when sampling without replacement, which both take the variance of the functions into account. While…
Classification with Asymmetric Label Noise: Consistency and Maximal Denoising
Gilles Blanchard, Marek Flaska, Gregory Handy +2
In many real-world classification problems, the labels of training examples are randomly corrupted. Most previous theoretical work on classification with label noise assumes that t…
The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning
Marius Kloft, Gilles Blanchard
We derive an upper bound on the local Rademacher complexity of -norm multiple kernel learning, which yields a tighter excess risk bound than global approaches. Previous loc…