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06/28/2023
Moment inequalities for sums of weakly dependent random fields
We derive both Azuma-Hoeffding and Burkholder-type inequalities for part...
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06/13/2023
False discovery proportion envelopes with consistency
We provide new false discovery proportion (FDP) confidence envelopes in ...
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06/07/2023
Label Shift Quantification with Robustness Guarantees via Distribution Feature Matching
Quantification learning deals with the task of estimating the target lab...
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06/05/2023
Covariance Adaptive Best Arm Identification
We consider the problem of best arm identification in the multi-armed ba...
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03/15/2023
Statistical learning on measures: an application to persistence diagrams
We consider a binary supervised learning classification problem where in...
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10/27/2021
Fast rates for prediction with limited expert advice
We investigate the problem of minimizing the excess generalization error...
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10/26/2021
Topologically penalized regression on manifolds
We study a regression problem on a compact manifold M. In order to take ...
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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...
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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...
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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...
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11/22/2020
Online Orthogonal Matching Pursuit
Greedy algorithms for feature selection are widely used for recovering s...
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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...
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04/17/2020
Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling
We provide statistical learning guarantees for two unsupervised learning...
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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...
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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...
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06/29/2019
Efficient Regularized Piecewise-Linear Regression Trees
We present a detailed analysis of the class of regression decision tree ...
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06/25/2019
Restless dependent bandits with fading memory
We study the stochastic multi-armed bandit problem in the case when the ...
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05/26/2019
Lepskii Principle in Supervised Learning
In the setting of supervised learning using reproducing kernel methods, ...
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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...
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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...
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07/04/2018
Post hoc false positive control for spatially structured hypotheses
In a high dimensional multiple testing framework, we present new confide...
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04/20/2018
On the Post Selection Inference constant under Restricted Isometry Properties
Uniformly valid confidence intervals post model selection in regression ...
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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...
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11/21/2017
Domain Generalization by Marginal Transfer Learning
Domain generalization is the problem of assigning class labels to an unl...
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10/19/2017
Early stopping for statistical inverse problems via truncated SVD estimation
We consider truncated SVD (or spectral cut-off, projection) estimators f...
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09/30/2017
Decontamination of Mutual Contamination Models
Many machine learning problems can be characterized by mutual contaminat...
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06/22/2017
Compressive Statistical Learning with Random Feature Moments
We describe a general framework --compressive statistical learning-- for...
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11/12/2016
Kernel regression, minimax rates and effective dimensionality: beyond the regular case
We investigate if kernel regularization methods can achieve minimax conv...
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10/24/2016
Parallelizing Spectral Algorithms for Kernel Learning
We consider a distributed learning approach in supervised learning for a...
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07/08/2016
Convergence rates of Kernel Conjugate Gradient for random design regression
We prove statistical rates of convergence for kernel-based least squares...
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04/14/2016
Optimal Rates For Regularization Of Statistical Inverse Learning Problems
We consider a statistical inverse learning problem, where we observe the...
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05/12/2015
Permutational Rademacher Complexity: a New Complexity Measure for Transductive Learning
Transductive learning considers situations when a learner observes m lab...
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11/26/2014
Localized Complexities for Transductive Learning
We show two novel concentration inequalities for suprema of empirical pr...
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07/18/2014
Extensions of stability selection using subsamples of observations and covariates
We introduce extensions of stability selection, a method to stabilise va...
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03/05/2013
Classification with Asymmetric Label Noise: Consistency and Maximal Denoising
In many real-world classification problems, the labels of training examp...
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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 ...
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09/29/2010
