| 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 | 849 | 2010 |
| Kernel mean embedding of distributions: A review and beyond K Muandet, K Fukumizu, B Sriperumbudur, B Schölkopf Foundations and Trends® in Machine Learning 10 (1-2), 1-141, 2017 | 809 | 2017 |
| 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 25, 2012 | 753 | 2012 |
| 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 | 742 | 2013 |
| Universality, Characteristic Kernels and RKHS Embedding of Measures. BK Sriperumbudur, K Fukumizu, GRG Lanckriet Journal of Machine Learning Research 12 (7), 2011 | 535 | 2011 |
| Gaussian processes and kernel methods: A review on connections and equivalences M Kanagawa, P Hennig, D Sejdinovic, BK Sriperumbudur arXiv preprint arXiv:1807.02582, 2018 | 360 | 2018 |
| On the empirical estimation of integral probability metrics BK Sriperumbudur, K Fukumizu, A Gretton, B Schölkopf, GRG Lanckriet | 350 | 2012 |
| On the Convergence of the Concave-Convex Procedure. BK Sriperumbudur, GRG Lanckriet Nips 9, 1759-1767, 2009 | 309 | 2009 |
| On the convergence of the concave-convex procedure G Lanckriet, BK Sriperumbudur Advances in neural information processing systems 22, 2009 | 273 | 2009 |
| A fast, consistent kernel two-sample test A Gretton, K Fukumizu, Z Harchaoui, BK Sriperumbudur Advances in neural information processing systems 22, 2009 | 269 | 2009 |
| Kernel choice and classifiability for RKHS embeddings of probability distributions K Fukumizu, A Gretton, G Lanckriet, B Schölkopf, BK Sriperumbudur Advances in neural information processing systems 22, 2009 | 266 | 2009 |
| Injective Hilbert space embeddings of probability measures BK Sriperumbudur, A Gretton, K Fukumizu, G Lanckriet, B Schölkopf 21st annual conference on learning theory (COLT 2008), 111-122, 2008 | 178 | 2008 |
| Learning theory for distribution regression Z Szabó, BK Sriperumbudur, B Póczos, A Gretton Journal of Machine Learning Research 17 (152), 1-40, 2016 | 175 | 2016 |
| Optimal rates for random Fourier features B Sriperumbudur, Z Szabó Advances in neural information processing systems 28, 2015 | 156 | 2015 |
| A majorization-minimization approach to the sparse generalized eigenvalue problem BK Sriperumbudur, DA Torres, GRG Lanckriet Machine learning 85, 3-39, 2011 | 140 | 2011 |
| Minimax estimation of maximum mean discrepancy with radial kernels IO Tolstikhin, BK Sriperumbudur, B Schölkopf Advances in Neural Information Processing Systems 29, 2016 | 130 | 2016 |
| Density estimation in infinite dimensional exponential families B Sriperumbudur, K Fukumizu, A Gretton, A Hyv, R Kumar Journal of Machine Learning Research 18 (57), 1-59, 2017 | 129* | 2017 |
| Sparse eigen methods by dc programming BK Sriperumbudur, DA Torres, GRG Lanckriet Proceedings of the 24th international conference on Machine learning, 831-838, 2007 | 115 | 2007 |
| Characteristic kernels on groups and semigroups K Fukumizu, A Gretton, B Schölkopf, BK Sriperumbudur Advances in neural information processing systems 21, 2008 | 112 | 2008 |
| Two-stage sampled learning theory on distributions Z Szabó, A Gretton, B Póczos, B Sriperumbudur Artificial Intelligence and Statistics, 948-957, 2015 | 102 | 2015 |
Kenji FukumizuThe Institute of Statistical Mathematics在 ism.ac.jp 的电子邮件经过验证
