Gilles Blanchard

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Clayton ScottProfessor of Electrical Engineering and Computer Science, University of MichiganVerified email at umich.edu
Klaus-Robert MüllerTU Berlin & Korea University & Google Brain & MPIIVerified 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 MückeTechnical University BerlinVerified email at math.tu-berlin.de
Olivier BousquetGoogleVerified email at m4x.org
Sylvain ArlotUniversité Paris-SaclayVerified email at universite-paris-saclay.fr
Dr. Nicole KrämerMorphoSysVerified email at morphosys.com
Vladimir SpokoinyProfessor for Statistics, Humboldt University und WIAS BerlinVerified email at wias-berlin.de
Marius KloftTU KaiserslauternVerified email at cs.uni-kl.de
Urun DoganResearcher, MicrosoftVerified email at microsoft.com
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
Gregory HandyDepartments of Neurobiology and Statistics, University of ChicagoVerified email at uchicago.edu
rozenholc yvesUniversité Paris DescartesVerified email at parisdescartes.fr
Christin Schäferacs plus GmbHVerified email at acs-plus.de
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


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Semi-supervised novelty detection G Blanchard, G Lee, C Scott Journal of Machine Learning Research 11, 2973-3009, 2010	243	2010
Statistical performance of support vector machines G Blanchard, O Bousquet, P Massart The Annals of Statistics 36 (2), 489-531, 2008	180	2008
Classification with asymmetric label noise: Consistency and maximal denoising C Scott, G Blanchard, G Handy Conference on Learning Theory (COLT 2013), 489-511, 2013	178	2013
Adaptive false discovery rate control under independence and dependence G Blanchard, É Roquain Journal of Machine Learning Research 10, 2837-2871, 2009	164	2009
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	163	2004
Statistical properties of kernel principal component analysis G Blanchard, O Bousquet, L Zwald Machine Learning 66 (2-3), 259-294, 2007	145	2007
On the rate of convergence of regularized boosting classifiers G Blanchard, G Lugosi, N Vayatis Journal of Machine Learning Research 4, 861-894, 2003	145	2003
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	142	2011
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	136	2006
Hierarchical testing designs for pattern recognition G Blanchard, D Geman The Annals of Statistics 33 (3), 1155-1202, 2005	105	2005
Two simple sufficient conditions for FDR control G Blanchard, E Roquain Electronic journal of statistics 2, 963-992, 2008	102	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	87	2006
Optimal rates for regularization of statistical inverse learning problems G Blanchard, N Mücke Foundations of Computational Mathematics 18 (4), 971-1013, 2018	77	2018
Novelty detection: Unlabeled data definitely help C Scott, G Blanchard AISTATS 2009, 464-471, 2009	75	2009
Optimal learning rates for kernel conjugate gradient regression G Blanchard, N Krämer Advances in Neural Information Processing Systems (NIPS 2010), 226-234, 2010	72	2010
Optimal dyadic decision trees G Blanchard, C Schäfer, Y Rozenholc, KR Müller Machine Learning 66 (2-3), 209-241, 2007	61	2007
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	59	2011
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	56	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	55	2010
Compressive statistical learning with random feature moments R Gribonval, G Blanchard, N Keriven, Y Traonmilin arXiv preprint arXiv:1706.07180, 2017	50	2017

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