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[1] N. Aronszajn. Theory of Reproducing Kernels. , 1950 .
[2] L. Klebanov,et al. A characterization of distributions by mean values of statistics and certain probabilistic metrics , 1992 .
[3] Jean-François Cardoso,et al. Multidimensional independent component analysis , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).
[4] Ingo Steinwart,et al. On the Influence of the Kernel on the Consistency of Support Vector Machines , 2002, J. Mach. Learn. Res..
[5] Dudley,et al. Real Analysis and Probability: Integration , 2002 .
[6] Anthony Widjaja,et al. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond , 2003, IEEE Transactions on Neural Networks.
[7] L. Baringhaus,et al. On a new multivariate two-sample test , 2004 .
[8] A. Berlinet,et al. Reproducing kernel Hilbert spaces in probability and statistics , 2004 .
[9] G. Székely,et al. TESTING FOR EQUAL DISTRIBUTIONS IN HIGH DIMENSION , 2004 .
[10] Bernhard Schölkopf,et al. Measuring Statistical Dependence with Hilbert-Schmidt Norms , 2005, ALT.
[11] Bernhard Schölkopf,et al. Kernel Methods for Measuring Independence , 2005, J. Mach. Learn. Res..
[12] Maria L. Rizzo,et al. A new test for multivariate normality , 2005 .
[13] Yuesheng Xu,et al. Universal Kernels , 2006, J. Mach. Learn. Res..
[14] Hans-Peter Kriegel,et al. Integrating structured biological data by Kernel Maximum Mean Discrepancy , 2006, ISMB.
[15] Le Song,et al. A Kernel Statistical Test of Independence , 2007, NIPS.
[16] Bernhard Schölkopf,et al. Kernel Measures of Conditional Dependence , 2007, NIPS.
[17] Le Song,et al. A Hilbert Space Embedding for Distributions , 2007, IFIP Working Conference on Database Semantics.
[18] Le Song,et al. A Hilbert Space Embedding for Distributions , 2007, Discovery Science.
[19] Zaïd Harchaoui,et al. Testing for Homogeneity with Kernel Fisher Discriminant Analysis , 2007, NIPS.
[20] Maria L. Rizzo,et al. Measuring and testing dependence by correlation of distances , 2007, 0803.4101.
[21] Andreas Christmann,et al. Support vector machines , 2008, Data Mining and Knowledge Discovery Handbook.
[22] C. Carmeli,et al. Vector valued reproducing kernel Hilbert spaces and universality , 2008, 0807.1659.
[23] Michael I. Jordan,et al. Kernel dimension reduction in regression , 2009, 0908.1854.
[24] Maria L. Rizzo,et al. Brownian distance covariance , 2009, 1010.0297.
[25] Kenji Fukumizu,et al. Universality, Characteristic Kernels and RKHS Embedding of Measures , 2010, J. Mach. Learn. Res..
[26] Le Song,et al. Kernelized Sorting , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[27] Bernhard Schölkopf,et al. Hilbert Space Embeddings and Metrics on Probability Measures , 2009, J. Mach. Learn. Res..
[28] Gilles Blanchard,et al. Generalizing from Several Related Classification Tasks to a New Unlabeled Sample , 2011, NIPS.
[29] Le Song,et al. Kernel Belief Propagation , 2011, AISTATS.
[30] Bernhard Schölkopf,et al. A Kernel Two-Sample Test , 2012, J. Mach. Learn. Res..
[31] Bernhard Schölkopf,et al. Learning from Distributions via Support Measure Machines , 2012, NIPS.
[32] Le Song,et al. Feature Selection via Dependence Maximization , 2012, J. Mach. Learn. Res..
[33] Kenji Fukumizu,et al. Equivalence of distance-based and RKHS-based statistics in hypothesis testing , 2012, ArXiv.
[34] Tapio Salakoski,et al. A Kernel-Based Framework for Learning Graded Relations From Data , 2011, IEEE Transactions on Fuzzy Systems.
[35] Bernhard Schölkopf,et al. Domain Adaptation under Target and Conditional Shift , 2013, ICML.
[36] Le Song,et al. Kernel Bayes' rule: Bayesian inference with positive definite kernels , 2013, J. Mach. Learn. Res..
[37] Arthur Gretton,et al. A Kernel Test for Three-Variable Interactions , 2013, NIPS.
[38] Joshua B. Tenenbaum,et al. Automatic Construction and Natural-Language Description of Nonparametric Regression Models , 2014, AAAI.
[39] A. Gretton. A simpler condition for consistency of a kernel independence test , 2015, 1501.06103.
[40] Bernhard Schölkopf,et al. Computing functions of random variables via reproducing kernel Hilbert space representations , 2015, Statistics and Computing.
[41] Bernhard Schölkopf,et al. Distinguishing Cause from Effect Using Observational Data: Methods and Benchmarks , 2014, J. Mach. Learn. Res..
[42] Wittawat Jitkrittum,et al. K2-ABC: Approximate Bayesian Computation with Kernel Embeddings , 2015, AISTATS.
[43] M. Urner. Scattered Data Approximation , 2016 .
[44] Oluwasanmi Koyejo,et al. Examples are not enough, learn to criticize! Criticism for Interpretability , 2016, NIPS.
[45] Qiang Liu,et al. A Kernelized Stein Discrepancy for Goodness-of-fit Tests , 2016, ICML.
[46] Kenji Fukumizu,et al. Persistence weighted Gaussian kernel for topological data analysis , 2016, ICML.
[47] Arthur Gretton,et al. A Kernel Test of Goodness of Fit , 2016, ICML.
[48] Arthur Gretton,et al. Learning Theory for Distribution Regression , 2014, J. Mach. Learn. Res..
[49] B. Schölkopf,et al. Kernel‐based tests for joint independence , 2016, 1603.00285.
[50] Kenji Fukumizu,et al. A Linear-Time Kernel Goodness-of-Fit Test , 2017, NIPS.
[51] Arthur Gretton,et al. An Adaptive Test of Independence with Analytic Kernel Embeddings , 2016, ICML.
[52] Johanna F. Ziegel,et al. Strictly proper kernel scores and characteristic kernels on compact spaces , 2017, Applied and Computational Harmonic Analysis.
[53] Alexander J. Smola,et al. Deep Sets , 2017, 1703.06114.
[54] Ann. Probab. Distance Covariance in Metric Spaces , 2017 .
[55] Bernhard Schölkopf,et al. Kernel Mean Embedding of Distributions: A Review and Beyonds , 2016, Found. Trends Mach. Learn..
[56] Eric V. Strobl,et al. Approximate Kernel-Based Conditional Independence Tests for Fast Non-Parametric Causal Discovery , 2017, Journal of Causal Inference.
[57] Kenji Fukumizu,et al. Post Selection Inference with Kernels , 2016, AISTATS.
[58] Bernhard Schölkopf,et al. Kernel Distribution Embeddings: Universal Kernels, Characteristic Kernels and Kernel Metrics on Distributions , 2016, J. Mach. Learn. Res..
[59] Dino Sejdinovic,et al. Bayesian Approaches to Distribution Regression , 2017, AISTATS.
[60] Gilles Blanchard,et al. Domain Generalization by Marginal Transfer Learning , 2017, J. Mach. Learn. Res..
[61] Krishnakumar Balasubramanian,et al. On the Optimality of Kernel-Embedding Based Goodness-of-Fit Tests , 2017, J. Mach. Learn. Res..