Seminar

There is an exciting fertile relationship between statistics/machine learning  and combinatorial geometry. My talk shows how variations of Tverberg’s theorem (to be  explained, don’t worry!) can answer several basic questions from machine learning and statistical inference. In particular, touches on the question: When do I have sufficient data in my sample to conclude learning was properly done?

I will not assume any prior knowledge. All new theorems are joint work with subsets of the following wonderful researchers: T. Hogan, D. Oliveros, E. Jaramillo-Rodriguez, and A. Torres-Hernandez.