G. Blanchard

发表

Statistical performance of support vector machines

P. Massart, O. Bousquet, G. Blanchard, 2008, 0804.0551.

Two simple sufficient conditions for FDR control

G. Blanchard, Étienne Roquain, 2008, 0802.1406.

Hierarchical testing designs for pattern recognition

D. Geman, G. Blanchard, 2005, math/0507421.

The “progressive mixture” estimator for regression trees

G. Blanchard, 1999 .

On the convergence rate of l p -norm multiple kernel learning

M. Kloft, G. Blanchard, 2012 .

Optimal dyadic decision trees

Klaus-Robert Müller, Gilles Blanchard, Christin Schäfer, 2007, Machine Learning.

On the Convergence of Eigenspaces in Kernel Principal Component Analysis

Gilles Blanchard, Laurent Zwald, G. Blanchard, 2005, NIPS.

Testing over a continuum of null hypotheses

G. Blanchard, Étienne Roquain, S. Delattre, 2011 .

ON LEAST FAVORABLE CONFIGURATIONS FOR STEP-UP-DOWN TESTS

G. Blanchard, Étienne Roquain, T. Dickhaus, 2011, 1108.5262.

Statistical properties of Kernel Prinicipal Component Analysis

Gilles Blanchard, Olivier Bousquet, Laurent Zwald, 2019 .

Adaptive False Discovery Rate Control under Independence and Dependence

Gilles Blanchard, Étienne Roquain, G. Blanchard, 2009, J. Mach. Learn. Res..

On the Rate of Convergence of Regularized Boosting Classifiers

Gilles Blanchard, Gábor Lugosi, Nicolas Vayatis, 2003, J. Mach. Learn. Res..

Classification with Asymmetric Label Noise: Consistency and Maximal Denoising

Gilles Blanchard, Clayton Scott, Gregory Handy, 2013, COLT.

with Asymmetric Label Noise: Consistency and Maximal Denoising

G. Blanchard, C. Scott, G. Handy, 2014 .

Post hoc false discovery proportion inference under a Hidden Markov Model

Gilles Blanchard, Etienne Roquain, Marie Perrot-Dockes, 2021, 2105.00288.

On the Post Selection Inference constant under Restricted Isometry Properties

G. Blanchard, P. Neuvial, F. Bachoc, 2018, 1804.07566.

Post hoc false positive control for structured hypotheses

G. Blanchard, Étienne Roquain, P. Neuvial, 2020, Scandinavian Journal of Statistics.

Restless dependent bandits with fading memory pdf

Gilles Blanchard, Alexandra Carpentier, Oleksandr Zadorozhnyi, 2019, ArXiv.

The f-Adjusted Graph Laplacian: a Diagonal Modification with a Geometric Interpretation

Ulrike von Luxburg, Gilles Blanchard, Sven Kurras, 2014, ICML.

Post hoc false positive control for spatially structured hypotheses

G. Blanchard, Étienne Roquain, P. Neuvial, 2018, 1807.01470.

Transductive anomaly detection

G. Blanchard, C. Scott, 2008 .

Parallelizing Spectral Algorithms for Kernel Learning

G. Blanchard, Nicole Mucke, 2016, 1610.07487.

M ay 2 01 9 LEPSKĬI PRINCIPLE IN SUPERVISED LEARNING

G. Blanchard, P. Mathé, Nicole Mücke, 2021 .

Discussion: Local Rademacher complexities and oracle inequalities in risk minimization

P. Massart, G. Blanchard, 2006 .

Discrepancy principle for statistical inverse problems with application to conjugate gradient iteration

G. Blanchard, P. Mathé, 2012 .

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

G. Blanchard, Nicole Mucke, 2016, 1611.03979.

Some nonasymptotic results on resampling in high dimension, I: Confidence regions, II: Multiple tests

G. Blanchard, Étienne Roquain, Sylvain Arlot, 2007, 0712.0775.

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

G. Blanchard, Abhishake Rastogi, Peter Math'e, 2019, 1902.05404.

Some nonasymptotic results on resampling in high dimension, II: Multiple tests

G. Blanchard, Étienne Roquain, Sylvain Arlot, 2010 .

Permutational Rademacher Complexity - A New Complexity Measure for Transductive Learning pdf

Gilles Blanchard, Nikita Zhivotovskiy, Ilya O. Tolstikhin, 2015, ALT.

Classication with Asymmetric Label Noise: Consistency

G. Blanchard, C. Scott, G. Handy, 2013 .

Mathematisches Forschungsinstitut Oberwolfach New Inference Concepts for Analysing Complex Data

G. Blanchard, D. Belomestny, Organised, 2022 .

2 5 Fe b 20 09 Kernel Conjugate Gradient is Universally Consistent

G. Blanchard, Nicole Krämer, 2009 .

Kernel Conjugate Gradient is Universally Consistent

G. Blanchard, Nicole Kraemer, 2009 .

Lepskii Principle in Supervised Learning

Gilles Blanchard, Nicole Mucke, Peter Math'e, 2019, 1905.10764.

New Horizons in Statistical Decision Theory 2299 Workshop : New Horizons in Statistical Decision Theory Table of Contents

G. Blanchard, 2015 .

A new algorithm of non-Gaussian component analysis with radial kernel functions

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2007 .

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

G. Blanchard, F. Göbel, 2019 .

SOME NONASYMPTOTIC RESULTS ON RESAMPLING IN HIGH DIMENSION, I: CONFIDENCE REGIONS'

G. Blanchard, Étienne Roquain, Sylvain Arlot, 2007, 0712.0775.

Non-asymptotic resampling-based confidence regions and multiple tests in high dimension

G. Blanchard, Étienne Roquain, Sylvain Arlot, 2007 .

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

G. Blanchard, Oleksandr Zadorozhnyi, 2017, Bernoulli.

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

G. Blanchard, Oleksandr Zadorozhnyi, 2017 .

Hierarchical Testing Designs for Pattern Recognition This Reprint Differs from the Original in Pagination and Typographic Detail. 1 2

D. Geman, G. Blanchard, 2003 .

On the Convergence Rate ofp-Norm Multiple Kernel Learning

M. Kloft, G. Blanchard, 2012 .

Topologically penalized regression on manifolds

G. Blanchard, W. Polonik, K. Balasubramanian, 2021, J. Mach. Learn. Res..

Random Moments for Sketched Mixture Learning

G. Blanchard, R. Gribonval, N. Keriven, 2017 .

Post hoc inference via joint family-wise error rate control

G. Blanchard, Étienne Roquain, P. Neuvial, 2017, 1703.02307.

SOME NONASYMPTOTIC RESULTS ON RESAMPLING IN HIGH DIMENSION, I: CONFIDENCE REGIONS 1

G. Blanchard, 2009 .

Statistical performance of support vector machines

P. Massart, O. Bousquet, G. Blanchard, 2008, 0804.0551.

ON LEAST FAVORABLE CONFIGURATIONS FOR STEP-UP-DOWN TESTS

G. Blanchard, Étienne Roquain, T. Dickhaus, 2011, 1108.5262.

On the convergence rate of lp-norm multiple kernel learning

Gilles Blanchard, Marius Kloft, M. Kloft, 2012, J. Mach. Learn. Res..

Hierarchical testing designs for pattern recognition

D. Geman, G. Blanchard, 2005, math/0507421.

Constant regret for sequence prediction with limited advice

G. Blanchard, E. Saad, 2022, ALT.

Extensions of stability selection using subsamples of observations and covariates

G. Blanchard, Ü. Dogan, A. Beinrucker, 2015, Statistics and Computing.

Optimal Rates for Regularization of Statistical Inverse Learning Problems

G. Blanchard, Nicole Mücke, 2017, Foundations of Computational Mathematics.

How wrong can we get? A review of machine learning approaches and error bars.

Gilles Blanchard, Sebastian Mika, Anton Schwaighofer, 2009, Combinatorial chemistry & high throughput screening.

Construction of Tight Frames on Graphs and Application to Denoising

Ulrike von Luxburg, Gilles Blanchard, G. Blanchard, 2014, 1408.4012.

Compressive Statistical Learning with Random Feature Moments pdf

Rémi Gribonval, Gilles Blanchard, Yann Traonmilin, 2017, Mathematical Statistics and Learning.

In Search of Non-Gaussian Components of a High-Dimensional Distribution

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2006, J. Mach. Learn. Res..

A Novel Dimension Reduction Procedure for Searching Non-Gaussian Subspaces

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2006, ICA.

Submitted to the Annals of Statistics SOME NON-ASYMPTOTIC RESULTS ON RESAMPLING IN HIGH DIMENSION , II : MULTIPLE TESTS By

G. Blanchard, Étienne Roquain, Sylvain Arlot, 2009 .

Some nonasymptotic results on resampling in high dimension, I: Confidence regions, II: Multiple tests

Gilles Blanchard, Etienne Roquain, Sylvain Arlot, 2007 .

Adaptive FDR control under independence and dependence

Gilles Blanchard, Etienne Roquain, G. Blanchard, 2007, 0707.0536.

Fe b 20 08 Self-consistent multiple testing procedures

G. Blanchard, Étienne Roquain, 2008 .

Domain Generalization by Marginal Transfer Learning

Gilles Blanchard, Gyemin Lee, Clayton Scott, 2017, J. Mach. Learn. Res..

Generalizing from Several Related Classification Tasks to a New Unlabeled Sample

Gilles Blanchard, Gyemin Lee, Clayton Scott, 2011, NIPS.

Submitted to the Annals of Statistics SOME NON-ASYMPTOTIC RESULTS ON RESAMPLING IN HIGH DIMENSION , I : CONFIDENCE REGIONS By

G. Blanchard, Étienne Roquain, Sylvain Arlot, 2009 .

Statistical properties of kernel principal component analysis

Gilles Blanchard, Olivier Bousquet, Laurent Zwald, 2007, Machine Learning.

Decontamination of Mutual Contamination Models

Gilles Blanchard, Clayton Scott, Julian Katz-Samuels, 2017, J. Mach. Learn. Res..

BCI competition 2003-data set IIa: spatial patterns of self-controlled brain rhythm modulations

Gilles Blanchard, Benjamin Blankertz, G. Blanchard, 2004, IEEE Transactions on Biomedical Engineering.

Testing over a continuum of null hypotheses with False Discovery Rate control

G. Blanchard, Étienne Roquain, S. Delattre, 2011, 1110.3599.

µTOSS - Multiple hypothesis testing in an open software system

Gilles Blanchard, Thorsten Dickhaus, Wiebke Werft, 2010, WAPA.

Discussion of ``2004 IMS Medallion Lecture: Local Rademacher complexities and oracle inequalities in risk minimization'' by V. Koltchinskii

Gilles Blanchard, Pascal Massart, P. Massart, 2007, 0708.0094.

Optimal Rates for Regularization of Statistical Inverse Learning Problems

Gilles Blanchard, Nicole Mücke, G. Blanchard, 2016, Found. Comput. Math..

Decontamination of Mutually Contaminated Models

Gilles Blanchard, Clayton Scott, G. Blanchard, 2014, AISTATS.

Semi-Supervised Novelty Detection

Gilles Blanchard, Gyemin Lee, Clayton Scott, 2010, J. Mach. Learn. Res..

Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies

Gilles Blanchard, Sören Sonnenburg, Arcadi Navarro, 2016, Scientific Reports.

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

G. Blanchard, P. Mathé, Abhishake Rastogi, 2019, Electronic Journal of Statistics.

Statistical Learning Guarantees for Compressive Clustering and Compressive Mixture Modeling

G. Blanchard, R. Gribonval, N. Keriven, 2020, Mathematical Statistics and Learning.

Post hoc confidence bounds on false positives using reference families

G. Blanchard, Étienne Roquain, P. Neuvial, 2020, The Annals of Statistics.

Post hoc false discovery proportion inference under a Hidden Markov Model

G. Blanchard, Étienne Roquain, P. Neuvial, 2021, 2105.00288.

A Simple Extension of Stability Feature Selection

Gilles Blanchard, Andre Beinrucker, Ürün Dogan, 2012, DAGM/OAGM Symposium.

Covariance Adaptive Best Arm Identification

G. Blanchard, N. Verzelen, E. Saad, 2023, ArXiv.

Online Orthogonal Matching Pursuit

G. Blanchard, Sylvain Arlot, E. Saad, 2020, ArXiv.

The Local Rademacher Complexity of Lp-Norm Multiple Kernel Learning

Gilles Blanchard, Marius Kloft, M. Kloft, 2011, NIPS.

Error rate control for classification rules in multiclass mixture models

Gilles Blanchard, Tristan Mary-Huard, Marie-Laure Martin-Magniette, 2021, The international journal of biostatistics.

Novelty detection: Unlabeled data definitely help

Gilles Blanchard, Clayton Scott, G. Blanchard, 2009, AISTATS.

Non-Gaussian Component Analysis: a Semi-parametric Framework for Linear Dimension Reduction

Motoaki Kawanabe, Klaus-Robert Müller, Masashi Sugiyama, 2005, NIPS.

A minimax near-optimal algorithm for adaptive rejection sampling pdf

Gilles Blanchard, Alexandra Carpentier, Joseph C. Lam, 2018, ALT.

Finite-Dimensional Projection for Classification and Statistical Learning

Gilles Blanchard, Laurent Zwald, G. Blanchard, 2008 .

False discovery proportion envelopes with consistency

G. Blanchard, Étienne Roquain, I. Meah, 2023, 2306.07819.

Moment inequalities for sums of weakly dependent random fields

A. Carpentier, G. Blanchard, O. Zadorozhnyi, 2023, 2306.16403.

Domain Generalization by Marginal Transfer Learning

G. Blanchard, C. Scott, Ü. Dogan, 2017, J. Mach. Learn. Res..

Statistical properties of kernel principal component analysis

O. Bousquet, G. Blanchard, Laurent Zwald, 2004, Machine Learning.

Localized Complexities for Transductive Learning

Gilles Blanchard, Marius Kloft, Ilya O. Tolstikhin, 2014, COLT.

Pattern Recognition from One Example by Chopping

Gilles Blanchard, François Fleuret, G. Blanchard, 2005, NIPS.

Oracle Bounds and Exact Algorithm for Dyadic Classification Trees

Gilles Blanchard, Christin Schäfer, Yves Rozenholc, 2004, COLT.

Generalizing from Several Related Classification Tasks to a New Unlabeled Sample

G. Blanchard, C. Scott, Gyemin Lee, 2011, NIPS.

Occam's Hammer

Gilles Blanchard, François Fleuret, G. Blanchard, 2006, COLT.

Optimal learning rates for Kernel Conjugate Gradient regression

Gilles Blanchard, Nicole Krämer, G. Blanchard, 2010, NIPS.

On the Convergence of Eigenspaces in Kernel Principal Component Analysis

G. Blanchard, Laurent Zwald, 2005, NIPS.

Minimax Euclidean separation rates for testing convex hypotheses in $\mathbb{R}^{d}$

Gilles Blanchard, Alexandra Carpentier, Maurilio Gutzeit, 2017, 1702.03760.

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

Hannah Marienwald, Jean-Baptiste Fermanian, Gilles Blanchard, 2020, AISTATS.

Extensions of stability selection using subsamples of observations and covariates

Gilles Blanchard, Andre Beinrucker, Ürün Dogan, 2014, Stat. Comput..