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

Michael Noukhovitch mnoukhov at gmail.com
Jeu 23 Nov 09:24:20 EST 2017


Reminder: this is in 1 hour!

On Tue, Nov 21, 2017, 10:30 Michael Noukhovitch, <mnoukhov at gmail.com> wrote:

> 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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