Gilles Blanchard - Google Scholar

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Citations	2830	1548
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Co-authors

Clayton ScottProfessor of Electrical Engineering and Computer Science, University of MichiganVerified email at umich.edu
Klaus-Robert MüllerGoogle Brain (on leave of Professor for Machine Learning, TU Berlin & Korea University, Seoul)Verified email at tu-berlin.de
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerified email at k.u-tokyo.ac.jp
Motoaki KawanabeSenior Researcher, Advanced Telecommunications Research Institute InternationalVerified email at atr.jp
Nicole MückeUniversity of StuttgartVerified email at mathematik.uni-stuttgart.de
Olivier BousquetGoogleVerified email at m4x.org
Vladimir SpokoinyProfessor for Statistics, Humboldt University und WIAS BerlinVerified email at wias-berlin.de
Dr. Nicole KrämerMorphoSysVerified email at morphosys.com
Sylvain ArlotUniversité Paris-SaclayVerified email at math.u-psud.fr
Marius KloftTU KaiserslauternVerified email at cs.uni-kl.de
Pierre NeuvialCNRS, Institut de Mathématiques de ToulouseVerified email at math.univ-toulouse.fr
Rémi GribonvalInria & ENS de LyonVerified email at ens-lyon.fr
Thorsten DickhausProfessor of Mathematical Statistics at University of BremenVerified email at uni-bremen.de
Urun DoganResearcher, MicrosoftVerified email at microsoft.com
rozenholc yvesUniversité Paris DescartesVerified email at parisdescartes.fr
Christin Schäferacs plus GmbHVerified email at acs-plus.de
Gregory HandyDepartment of Mathematics, University of PittsburghVerified email at pitt.edu
Nicolas KerivenCNRS, GIPSA-labVerified email at gipsa-lab.grenoble-inp.fr
Yann TraonmilinCNRS researcher Institut de Mathématiques de BordeauxVerified email at u-bordeaux.fr
François FleuretUniversity of GenevaVerified email at fleuret.org

Gilles Blanchard

Professor of Statistics, University of Paris-Saclay

Verified email at math.u-psud.fr - Homepage

Statistics Machine Learning Pattern Recognition Statistical Learning Theory


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Semi-supervised novelty detection G Blanchard, G Lee, C Scott Journal of Machine Learning Research 11, 2973-3009, 2010	186	2010
Statistical performance of support vector machines G Blanchard, O Bousquet, P Massart The Annals of Statistics 36 (2), 489-531, 2008	173	2008
BCI competition 2003-data set IIa: spatial patterns of self-controlled brain rhythm modulations G Blanchard, B Blankertz IEEE Transactions on Biomedical Engineering 51 (6), 1062-1066, 2004	152	2004
Adaptive false discovery rate control under independence and dependence G Blanchard, É Roquain Journal of Machine Learning Research 10, 2837-2871, 2009	138	2009
On the rate of convergence of regularized boosting classifiers G Blanchard, G Lugosi, N Vayatis Journal of Machine Learning Research 4, 861-894, 2003	135	2003
On the convergence of eigenspaces in kernel principal component analysis L Zwald, G Blanchard Advances in Neural Information Processing Systems (NIPS 2005) 18, 1649-1656, 2006	130	2006
Classification with asymmetric label noise: Consistency and maximal denoising C Scott, G Blanchard, G Handy Conference on Learning Theory (COLT 2013), 489-511, 2013	129	2013
Statistical properties of kernel principal component analysis G Blanchard, O Bousquet, L Zwald Machine Learning 66 (2-3), 259-294, 2007	129	2007
Hierarchical testing designs for pattern recognition G Blanchard, D Geman The Annals of Statistics 33 (3), 1155-1202, 2005	95	2005
Generalizing from several related classification tasks to a new unlabeled sample G Blanchard, G Lee, C Scott Advances in neural information processing systems (NIPS 2011), 2178-2186, 2011	89	2011
Two simple sufficient conditions for FDR control G Blanchard, E Roquain Electronic journal of statistics 2, 963-992, 2008	85	2008
In search of non-Gaussian components of a high-dimensional distribution G Blanchard, M Kawanabe, M Sugiyama, V Spokoiny, KR Müller Journal of Machine Learning Research 7, 247-282, 2006	83	2006
Optimal rates for regularization of statistical inverse learning problems G Blanchard, N Mücke Foundations of Computational Mathematics 18 (4), 971-1013, 2018	62	2018
Optimal learning rates for kernel conjugate gradient regression G Blanchard, N Krämer Advances in Neural Information Processing Systems (NIPS 2010), 226-234, 2010	62	2010
Novelty detection: Unlabeled data definitely help C Scott, G Blanchard AISTATS 2009, 464-471, 2009	60	2009
Optimal dyadic decision trees G Blanchard, C Schäfer, Y Rozenholc, KR Müller Machine Learning 66 (2-3), 209-241, 2007	58	2007
Exemplar-based image inpainting: Fast priority and coherent nearest neighbor search R Martínez-Noriega, A Roumy, G Blanchard 2012 IEEE International workshop on machine learning for signal processing, 1-6, 2012	52	2012
Some nonasymptotic results on resampling in high dimension, I: Confidence regions S Arlot, G Blanchard, E Roquain The Annals of Statistics 38 (1), 51-82, 2010	49	2010
The local Rademacher complexity of lp-norm multiple kernel learning M Kloft, G Blanchard Advances in Neural Information Processing Systems (NIPS 2011), 2438-2446, 2011	47	2011
Statistical properties of kernel principal component analysis L Zwald, O Bousquet, G Blanchard Learning theory (COLT 2004), 594-608, 2004	46	2004

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