[Lisa_seminaires] [Tea Talk] Martin Arjovsky (NYU) Thr Nov 23, 10:30AM, AA6214

Michael Noukhovitch mnoukhov at gmail.com
Mar 21 Nov 10:30:57 EST 2017


This week we have a former MILA intern, *Martin Arjovsky,* now at NYU
giving a talk on *Thursday Nov 23* at* 10:30AM* in room *AA6214*.

Be there or be probabilistically sqaure!
Michael

*KEYWORDS* deep learning theory, probability theory, geometry

*TITLE* A few geometrical insights into unsupervised learning

*ABSTRACT*
This short talk will address the connections and properties of the energy
(including Cramer), and Wasserstein distances, and how they connect with
Maximum Mean Discrepancy. We will dive into the similarities and
differences among them, and how this impacts their behaviour in different
ways. Some of the results are a contextualization of old studies, and some
will be presented in this talk for the first time. A recurrent theme in the
talk will be the study of the geometry of the space of probability
distributions, and how this might shed clarity on the behaviour of
different algorithms in practice.

*BIO*
I'm Martin Arjovsky, I'm currently doing my PhD at New York University, and
being advised by Léon Bottou. I did my undergraduate and master's in the
University of Buenos Aires, Argentina (my home country). In the middle I
took a year off to do internships in different places (Google, Facebook,
Microsoft, and the Université de Montréal). My master's thesis advisor was
Yoshua Bengio, who also advised me during my stay at UdeM.
In general I'm interested in the intersection between learning and
mathematics, how we can ground the different learning processes that are
involved in different problems, and leverage this knowledge to develop
better algorithms.
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