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 | 499 | 2011 |
Semi-supervised novelty detection G Blanchard, G Lee, C Scott Journal of Machine Learning Research 11, 2973-3009, 2010 | 333 | 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 | 263 | 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 | 243 | 2013 |
Statistical performance of support vector machines G Blanchard, O Bousquet, P Massart The Annals of Statistics 36 (2), 489-531, 2008 | 209 | 2008 |
Adaptive false discovery rate control under independence and dependence G Blanchard, É Roquain Journal of Machine Learning Research 10, 2837-2871, 2009 | 204 | 2009 |
Statistical properties of kernel principal component analysis G Blanchard, O Bousquet, L Zwald Machine Learning 66 (2-3), 259-294, 2007 | 185 | 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 | 175 | 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 | 163 | 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 | 151 | 2018 |
Two simple sufficient conditions for FDR control G Blanchard, E Roquain Electronic journal of statistics 2, 963-992, 2008 | 128 | 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 | 99 | 2006 |
Optimal learning rates for kernel conjugate gradient regression G Blanchard, N Krämer Advances in Neural Information Processing Systems (NIPS 2010), 226-234, 2010 | 90 | 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 | 86 | 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 | 72* | 2018 |