Carl-Johann Simon-Gabriel (Tubingen)
Aug 29, 2016, 3-4pm; 400 Cory Hall
Title and Abstract
Kernel Mean Embeddings: a Quick Guided Tour
After a brief introduction to kernel mean embeddings and to their use, we will see that dk metrizes the weak convergence of probability measures whenever k is continuous and characteristic. We will then systematically link characteristic kernels to the more traditional notions of universal and/or strictly positive definite kernels. These links will show that many kernel mean embeddings can be extended to embed (injectively!) spaces of Schwartz-distributions, i.e. generalized measures.
After his master in “Geostatistics and Applied Probabilities” from Mines Paristech (France), Carl-Johann Simon-Gabriel joined Bernhard Schölkopf's group (Empirical Inference Department, Max Planck Institute for Intelligent Systems, Tübingen, Germany) in 2013 to work as a PhD student on both causal inference and kernel methods.