Gilles Blanchard

research

∙ 06/28/2023

Moment inequalities for sums of weakly dependent random fields

We derive both Azuma-Hoeffding and Burkholder-type inequalities for part...

0 Gilles Blanchard, et al. ∙

research

∙ 06/13/2023

False discovery proportion envelopes with consistency

We provide new false discovery proportion (FDP) confidence envelopes in ...

0 Iqraa Meah, et al. ∙

research

∙ 06/07/2023

Label Shift Quantification with Robustness Guarantees via Distribution Feature Matching

Quantification learning deals with the task of estimating the target lab...

0 Bastien Dussap, et al. ∙

research

∙ 06/05/2023

Covariance Adaptive Best Arm Identification

We consider the problem of best arm identification in the multi-armed ba...

0 El Mehdi Saad, et al. ∙

research

∙ 03/15/2023

Statistical learning on measures: an application to persistence diagrams

We consider a binary supervised learning classification problem where in...

0 Olympio Hacquard, et al. ∙

research

∙ 10/27/2021

Fast rates for prediction with limited expert advice

We investigate the problem of minimizing the excess generalization error...

0 El Mehdi Saad, et al. ∙

research

∙ 10/26/2021

Topologically penalized regression on manifolds

We study a regression problem on a compact manifold M. In order to take ...

0 Olympio Hacquard, et al. ∙

research

∙ 09/29/2021

Error rate control for classification rules in multiclass mixture models

In the context of finite mixture models one considers the problem of cla...

0 Tristan Mary-Huard, et al. ∙

research

∙ 09/01/2021

Nonasymptotic one-and two-sample tests in high dimension with unknown covariance structure

Let 𝐗 = (X_i)_1≤ i ≤ n be an i.i.d. sample of square-integrable variable...

0 Gilles Blanchard, et al. ∙

research

∙ 05/01/2021

Post hoc false discovery proportion inference under a Hidden Markov Model

We address the multiple testing problem under the assumption that the tr...

0 Marie Perrot-Dockès, et al. ∙

research

∙ 11/22/2020

Online Orthogonal Matching Pursuit

Greedy algorithms for feature selection are widely used for recovering s...

0 El Mehdi Saad, et al. ∙

research

∙ 11/13/2020

High-Dimensional Multi-Task Averaging and Application to Kernel Mean Embedding

We propose an improved estimator for the multi-task averaging problem, w...

0 Hannah Marienwald, et al. ∙

research

∙ 04/17/2020

Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling

We provide statistical learning guarantees for two unsupervised learning...

0 Rémi Gribonval, et al. ∙

research

∙ 10/25/2019

On agnostic post hoc approaches to false positive control

This document is a book chapter which gives a partial survey on post hoc...

0 Gilles Blanchard, et al. ∙

research

∙ 07/06/2019

Volume Doubling Condition and a Local Poincaré Inequality on Unweighted Random Geometric Graphs

The aim of this paper is to establish two fundamental measure-metric pro...

0 Franziska Göbel, et al. ∙

research

∙ 06/29/2019

Efficient Regularized Piecewise-Linear Regression Trees

We present a detailed analysis of the class of regression decision tree ...

0 Leonidas Lefakis, et al. ∙

research

∙ 06/25/2019

Restless dependent bandits with fading memory

We study the stochastic multi-armed bandit problem in the case when the ...

0 Oleksandr Zadorozhnyi, et al. ∙

research

∙ 05/26/2019

Lepskii Principle in Supervised Learning

In the setting of supervised learning using reproducing kernel methods, ...

0 Gilles Blanchard, et al. ∙

research

∙ 02/14/2019

Convergence analysis of Tikhonov regularization for non-linear statistical inverse learning problems

We study a non-linear statistical inverse learning problem, where we obs...

0 Abhishake Rastogi, et al. ∙

research

∙ 10/22/2018

A minimax near-optimal algorithm for adaptive rejection sampling

Rejection Sampling is a fundamental Monte-Carlo method. It is used to sa...

0 Juliette Achdou, et al. ∙

research

∙ 07/04/2018

Post hoc false positive control for spatially structured hypotheses

In a high dimensional multiple testing framework, we present new confide...

0 Guillermo Durand, et al. ∙

research

∙ 04/20/2018

On the Post Selection Inference constant under Restricted Isometry Properties

Uniformly valid confidence intervals post model selection in regression ...

0 François Bachoc, et al. ∙

research

∙ 12/05/2017

Concentration of weakly dependent Banach-valued sums and applications to kernel learning methods

We obtain a new Bernstein-type inequality for sums of Banach-valued rand...

0 Gilles Blanchard, et al. ∙

research

∙ 11/21/2017

Domain Generalization by Marginal Transfer Learning

Domain generalization is the problem of assigning class labels to an unl...

0 Gilles Blanchard, et al. ∙

research

∙ 10/19/2017

Early stopping for statistical inverse problems via truncated SVD estimation

We consider truncated SVD (or spectral cut-off, projection) estimators f...

0 Gilles Blanchard, et al. ∙

research

∙ 09/30/2017

Decontamination of Mutual Contamination Models

Many machine learning problems can be characterized by mutual contaminat...

0 Julian Katz-Samuels, et al. ∙

research

∙ 06/22/2017

Compressive Statistical Learning with Random Feature Moments

We describe a general framework --compressive statistical learning-- for...

0 Rémi Gribonval, et al. ∙

research

∙ 11/12/2016

Kernel regression, minimax rates and effective dimensionality: beyond the regular case

We investigate if kernel regularization methods can achieve minimax conv...

0 Gilles Blanchard, et al. ∙

research

∙ 10/24/2016

Parallelizing Spectral Algorithms for Kernel Learning

We consider a distributed learning approach in supervised learning for a...

0 Gilles Blanchard, et al. ∙

research

∙ 07/08/2016

Convergence rates of Kernel Conjugate Gradient for random design regression

We prove statistical rates of convergence for kernel-based least squares...

0 Gilles Blanchard, et al. ∙

research

∙ 04/14/2016

Optimal Rates For Regularization Of Statistical Inverse Learning Problems

We consider a statistical inverse learning problem, where we observe the...

0 Gilles Blanchard, et al. ∙

research

∙ 05/12/2015

Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning

Transductive learning considers situations when a learner observes m lab...

0 Ilya Tolstikhin, et al. ∙

research

∙ 11/26/2014

Localized Complexities for Transductive Learning

We show two novel concentration inequalities for suprema of empirical pr...

0 Ilya Tolstikhin, et al. ∙

research

∙ 07/18/2014

Extensions of stability selection using subsamples of observations and covariates

We introduce extensions of stability selection, a method to stabilise va...

0 Andre Beinrucker, et al. ∙

research

∙ 03/05/2013

Classification with Asymmetric Label Noise: Consistency and Maximal Denoising

In many real-world classification problems, the labels of training examp...

0 Gilles Blanchard, et al. ∙

research

∙ 03/03/2011

The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning

We derive an upper bound on the local Rademacher complexity of ℓ_p-norm ...

0 Marius Kloft, et al. ∙

research

∙ 09/29/2010

Optimal learning rates for Kernel Conjugate Gradient regression

We prove rates of convergence in the statistical sense for kernel-based ...

0 Gilles Blanchard, et al. ∙

Gilles Blanchard

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