| A kernel two-sample test A Gretton, KM Borgwardt, MJ Rasch, B Schölkopf, A Smola The Journal of Machine Learning Research 13 (1), 723-773, 2012 | 2271 | 2012 |
| Correcting sample selection bias by unlabeled data J Huang, A Gretton, K Borgwardt, B Schölkopf, A Smola Advances in neural information processing systems 19, 601-608, 2006 | 1423 | 2006 |
| A kernel method for the two-sample-problem A Gretton, K Borgwardt, M Rasch, B Schölkopf, A Smola Advances in neural information processing systems 19, 513-520, 2006 | 1315 | 2006 |
| Measuring statistical dependence with Hilbert-Schmidt norms A Gretton, O Bousquet, A Smola, B Schölkopf International conference on algorithmic learning theory, 63-77, 2005 | 1119 | 2005 |
| Ranking on data manifolds D Zhou, J Weston, A Gretton, O Bousquet, B Schölkopf Advances in neural information processing systems 16, 169-176, 2004 | 858 | 2004 |
| Integrating structured biological data by kernel maximum mean discrepancy KM Borgwardt, A Gretton, MJ Rasch, HP Kriegel, B Schölkopf, AJ Smola Bioinformatics 22 (14), e49-e57, 2006 | 783 | 2006 |
| A Hilbert space embedding for distributions A Smola, A Gretton, L Song, B Schölkopf International Conference on Algorithmic Learning Theory, 13-31, 2007 | 686 | 2007 |
| A kernel statistical test of independence. A Gretton, K Fukumizu, CH Teo, L Song, B Schölkopf, AJ Smola Nips 20, 585-592, 2007 | 564 | 2007 |
| Hilbert space embeddings and metrics on probability measures BK Sriperumbudur, A Gretton, K Fukumizu, B Schölkopf, GRG Lanckriet The Journal of Machine Learning Research 11, 1517-1561, 2010 | 535 | 2010 |
| Kernel measures of conditional dependence. K Fukumizu, A Gretton, X Sun, B Schölkopf NIPS 20, 489-496, 2007 | 439 | 2007 |
| Covariate shift by kernel mean matching A Gretton, A Smola, J Huang, M Schmittfull, K Borgwardt, B Schölkopf Dataset shift in machine learning 3 (4), 5, 2009 | 437 | 2009 |
| Equivalence of distance-based and RKHS-based statistics in hypothesis testing D Sejdinovic, B Sriperumbudur, A Gretton, K Fukumizu The Annals of Statistics, 2263-2291, 2013 | 399 | 2013 |
| Low-frequency local field potentials and spikes in primary visual cortex convey independent visual information A Belitski, A Gretton, C Magri, Y Murayama, MA Montemurro, ... Journal of Neuroscience 28 (22), 5696-5709, 2008 | 399 | 2008 |
| Optimal kernel choice for large-scale two-sample tests A Gretton, D Sejdinovic, H Strathmann, S Balakrishnan, M Pontil, ... Advances in neural information processing systems, 1205-1213, 2012 | 350 | 2012 |
| Kernel methods for measuring independence A Gretton, R Herbrich, A Smola, O Bousquet, B Schölkopf MIT Press, 2005 | 328 | 2005 |
| Feature selection via dependence maximization. L Song, A Smola, A Gretton, J Bedo, K Borgwardt Journal of Machine Learning Research 13 (5), 2012 | 324 | 2012 |
| Supervised feature selection via dependence estimation L Song, A Smola, A Gretton, KM Borgwardt, J Bedo Proceedings of the 24th international conference on Machine learning, 823-830, 2007 | 308 | 2007 |
| Statistical Consistency of Kernel Canonical Correlation Analysis. K Fukumizu, FR Bach, A Gretton Journal of Machine Learning Research 8 (2), 2007 | 262 | 2007 |
| Demystifying mmd gans M Bińkowski, DJ Sutherland, M Arbel, A Gretton arXiv preprint arXiv:1801.01401, 2018 | 255 | 2018 |
| Inferring spike trains from local field potentials MJ Rasch, A Gretton, Y Murayama, W Maass, NK Logothetis Journal of neurophysiology 99 (3), 1461-1476, 2008 | 225 | 2008 |
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de
