Gilles Blanchard

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Clayton ScottProfessor of Electrical Engineering and Computer Science, University of MichiganVerificeret mail på umich.edu
Klaus-Robert MüllerGoogle DeepMind & TU Berlin & Korea University & Max Planck Institute for Informatics, GermanyVerificeret mail på tu-berlin.de
Gyemin LeeSeoul National University of Science and TechnologyVerificeret mail på seoultech.ac.kr
Pierre NeuvialCNRS, Institut de Mathématiques de ToulouseVerificeret mail på math.univ-toulouse.fr
Masashi SugiyamaDirector, RIKEN Center for Advanced Intelligence Project / Professor, The University of TokyoVerificeret mail på k.u-tokyo.ac.jp
Motoaki KawanabeSenior Researcher, Advanced Telecommunications Research Institute InternationalVerificeret mail på atr.jp
Nicole MückeTechnical University BrunswickVerificeret mail på tu-braunschweig.de
Sylvain ArlotUniversité Paris-SaclayVerificeret mail på universite-paris-saclay.fr
Olivier BousquetGoogleVerificeret mail på m4x.org
Dr. Nicole KrämerBoehringer IngelheimVerificeret mail på boehringer-ingelheim.com
Marius KloftProfessor, RPTU Kaiserslautern-LandauVerificeret mail på cs.uni-kl.de
Vladimir SpokoinyProfessor for Statistics, Humboldt University und WIAS BerlinVerificeret mail på wias-berlin.de
Rémi GribonvalInria & ENS de LyonVerificeret mail på ens-lyon.fr
Thorsten DickhausProfessor of Mathematical Statistics at University of BremenVerificeret mail på uni-bremen.de
Gregory HandySchool of Mathematics, University of MinnesotaVerificeret mail på umn.edu
Nicolas KerivenCNRS, IRISAVerificeret mail på cnrs.fr
Yann TraonmilinCNRS researcher Institut de Mathématiques de BordeauxVerificeret mail på u-bordeaux.fr
rozenholc yvesUniversité Paris DescartesVerificeret mail på parisdescartes.fr
Christin Schäferacs plus GmbHVerificeret mail på acs-plus.de
François FleuretUniversity of GenevaVerificeret mail på fleuret.org

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Gilles Blanchard

Professor of Statistics, University of Paris-Saclay

Verificeret mail på universite-paris-saclay.fr - Startside

Statistics Machine Learning Pattern Recognition Statistical Learning Theory


Titel Sortér efter henvisninger Sortér efter årstal Sortér efter titel	Citeret af Citeret af	År
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	516	2011
Semi-supervised novelty detection G Blanchard, G Lee, C Scott Journal of Machine Learning Research 11, 2973-3009, 2010	337	2010
Domain generalization by marginal transfer learning G Blanchard, AA Deshmukh, U Dogan, G Lee, C Scott Journal of Machine Learning Research 22, 1-55, 2021	278	2021
Classification with asymmetric label noise: Consistency and maximal denoising C Scott, G Blanchard, G Handy Conference on Learning Theory (COLT 2013), 489-511, 2013	247	2013
Statistical performance of support vector machines G Blanchard, O Bousquet, P Massart The Annals of Statistics 36 (2), 489-531, 2008	213	2008
Adaptive false discovery rate control under independence and dependence G Blanchard, É Roquain Journal of Machine Learning Research 10, 2837-2871, 2009	205	2009
Statistical properties of kernel principal component analysis G Blanchard, O Bousquet, L Zwald Machine Learning 66 (2-3), 259-294, 2007	187	2007
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	177	2004
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	164	2006
On the rate of convergence of regularized boosting classifiers G Blanchard, G Lugosi, N Vayatis Journal of Machine Learning Research 4, 861-894, 2003	158	2003
Optimal rates for regularization of statistical inverse learning problems G Blanchard, N Mücke Foundations of Computational Mathematics 18 (4), 971-1013, 2018	156	2018
Two simple sufficient conditions for FDR control G Blanchard, E Roquain Electronic journal of statistics 2, 963-992, 2008	132	2008
Hierarchical testing designs for pattern recognition G Blanchard, D Geman The Annals of Statistics 33 (3), 1155-1202, 2005	105	2005
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	100	2006
Optimal learning rates for kernel conjugate gradient regression G Blanchard, N Krämer Advances in Neural Information Processing Systems (NIPS 2010), 226-234, 2010	91	2010
Novelty detection: Unlabeled data definitely help C Scott, G Blanchard AISTATS 2009, 464-471, 2009	89	2009
Combining multiple hypothesis testing with machine learning increases the statistical power of genome-wide association studies B Mieth, M Kloft, JA Rodríguez, S Sonnenburg, R Vobruba, ... Scientific reports 6 (1), 36671, 2016	87	2016
Compressive statistical learning with random feature moments R Gribonval, G Blanchard, N Keriven, Y Traonmilin Mathematical Statistics and Learning 3 (2), 113-164, 2021	74	2021
Post hoc confidence bounds on false positives using reference families G Blanchard, P Neuvial, E Roquain Annals of Statistics 48 (3), 1281-1303, 2020	74*	2020
Parallelizing spectrally regularized kernel algorithms N Mücke, G Blanchard Journal of Machine Learning Research 19 (30), 1-29, 2018	73*	2018

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