N. Aronszajn, Theory of reproducing kernels. Transactions of the, pp.337-404, 1950.

C. Carmeli, E. D. Vito, A. Toigo, and V. Umanitá, VECTOR VALUED REPRODUCING KERNEL HILBERT SPACES AND UNIVERSALITY, Analysis and Applications, vol.2, issue.01, pp.19-61, 2010.
DOI : 10.1007/s00041-007-9003-z

K. Fukumizu, A. Gretton, X. Sun, and B. Schölkopf, Kernel measures of conditional dependence, Neural Information Processing Systems (NIPS), pp.498-496, 2008.

K. Fukumizu, F. Bach, and M. Jordan, Kernel dimension reduction in regression. The Annals of Statistics, pp.1871-1905, 2009.

K. Fukumizu, L. Song, and A. Gretton, Kernel Bayes' rule: Bayesian inference with positive definite kernels, Journal of Machine Learning Research, vol.14, pp.3753-3783, 2013.

A. Gretton, A simpler condition for consistency of a kernel independence test, 2015.

A. Gretton, O. Bousquet, A. Smola, and B. Schölkopf, Measuring Statistical Dependence with Hilbert-Schmidt Norms, Algorithmic Learning Theory (ALT), pp.63-78, 2005.
DOI : 10.1007/11564089_7
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.105.477

A. Gretton, K. Fukumizu, C. H. Teo, L. Song, B. Schölkopf et al., A kernel statistical test of independence, Neural Information Processing Systems (NIPS), pp.585-592, 2008.

A. Gretton, K. M. Borgwardt, M. J. Rasch, B. Schölkopf, and A. Smola, A kernel two-sample test, Journal of Machine Learning Research, vol.13, pp.723-773, 2012.

G. Kusano, K. Fukumizu, and Y. Hiraoka, Persistence weighted Gaussian kernel for topological data analysis, International Conference on Machine Learning (ICML), pp.2004-2013, 2016.

J. Lloyd, D. Duvenaud, R. Grosse, J. B. Tenenbaum, and Z. Ghahramani, Automatic construction and natural-language description of nonparametric regression models, AAAI Conference on Artificial Intelligence, pp.1242-1250, 2014.

R. Lyons, Distance covariance in metric spaces. The Annals of Probability, pp.3284-3305, 2013.
DOI : 10.1214/12-aop803
URL : http://arxiv.org/abs/1106.5758

C. A. Micchelli, Y. Xu, and H. Zhang, Universal kernels, Journal of Machine Learning Research, vol.7, pp.2651-2667, 2006.

M. Joris, J. Mooij, D. Peters, J. Janzing, B. Zscheischler et al., Distinguishing cause from effect using observational data: Methods and benchmarks, Journal of Machine Learning Research, vol.17, pp.1-102, 2016.

K. Muandet, K. Fukumizu, F. Dinuzzo, and B. Schölkopf, Learning from distributions via support measure machines, Neural Information Processing Systems (NIPS), pp.10-18, 2011.

K. Muandet, K. Fukumizu, B. Sriperumbudur, and B. Schölkopf, Kernel Mean Embedding of Distributions: A Review and Beyond, Machine Learning, pp.1-141, 2017.
DOI : 10.1561/2200000060

M. Park, W. Jitkrittum, and D. Sejdinovic, K2-ABC: Approximate Bayesian computation with kernel embeddings, International Conference on Artificial Intelligence and Statistics (AISTATS; PMLR), pp.51398-407, 2016.

N. Pfister, P. Bühlmann, B. Schölkopf, and J. Peters, Kernel-based tests for joint independence, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.27, pp.2017-1467
DOI : 10.1214/12-AOS1041

B. Schölkopf and A. J. Smola, Learning with Kernels: Support Vector Machines , Regularization, Optimization, and Beyond, 2002.

B. Schölkopf, K. Muandet, K. Fukumizu, S. Harmeling, and J. Peters, Computing functions of random variables via reproducing kernel Hilbert space representations, Statistics and Computing, vol.2, issue.3, pp.755-766, 2015.
DOI : 10.1137/0114046

D. Sejdinovic, K. Bharath, A. Sriperumbudur, K. Gretton, and . Fukumizu, Equivalence of distance-based and RKHS-based statistics in hypothesis testing, The Annals of Statistics, vol.41, issue.5, pp.2263-2291, 2013.
DOI : 10.1214/13-AOS1140

C. Simon-gabriel and B. Schölkopf, Kernel distribution embeddings: Universal kernels, characteristic kernels and kernel metrics on distributions, Max Planck Institute for Intelligent Systems, 2016.

A. Smola, A. Gretton, . Le, B. Song, and . Schölkopf, A Hilbert space embedding for distributions, Algorithmic Learning Theory (ALT), pp.13-31, 2007.

L. Song, A. Gretton, D. Bickson, Y. Low, and C. Guestrin, Kernel belief propagation, International Conference on Artificial Intelligence and Statistics (AIS- TATS), pp.707-715, 2011.

L. Song, A. Smola, A. Gretton, J. Bedo, and K. Borgwardt, Feature selection via dependence maximization, Journal of Machine Learning Research, vol.13, pp.1393-1434, 2012.

K. Bharath, A. Sriperumbudur, K. Gretton, B. Fukumizu, G. R. Schölkopf et al., Hilbert space embeddings and metrics on probability measures, Journal of Machine Learning Research, vol.11, pp.1517-1561, 2010.

K. Bharath, K. Sriperumbudur, G. R. Fukumizu, and . Lanckriet, Universality, characteristic kernels and RKHS embedding of measures, Journal of Machine Learning Research, vol.12, pp.2389-2410, 2011.

I. Steinwart, On the influence of the kernel on the consistency of support vector machines, Journal of Machine Learning Research, vol.6, issue.3, pp.67-93, 2001.

Z. Szabó, B. Sriperumbudur, B. Póczos, and A. Gretton, Learning theory for distribution regression, Journal of Machine Learning Research, vol.17, issue.152, pp.1-40, 2016.

W. Waegeman, T. Pahikkala, A. Airola, T. Salakoski, M. Stock et al., A Kernel-Based Framework for Learning Graded Relations From Data, IEEE Transactions on Fuzzy Systems, vol.20, issue.6, pp.1090-1101, 2012.
DOI : 10.1109/TFUZZ.2012.2194151

H. Wendland, Scattered Data Approximation, Cambridge Monographs on Applied and Computational Mathematics, 2005.
DOI : 10.1017/CBO9780511617539

M. Yamada, Y. Umezu, K. Fukumizu, and I. Takeuchi, Post selection inference with kernels

K. Zhang, B. Schölkopf, K. Muandet, and Z. Wang, Domain adaptation under target and conditional shift, Journal of Machine Learning Research, vol.28, issue.3, pp.819-827, 2013.