[Todos] CORRECCIÓN: doble Seminario de Probabilidad del DM.

[Manuel Saenz]

Ambas charlas serán el miércoles 20 de Noviembre próximo.

Saludos.

El jue., 14 nov. 2019 a las 18:41, Manuel Saenz (<saenz.manuel en gmail.com>)
escribió:

> Les informamos que la semana próxima habrá *SESIÓN DOBLE *del Seminario de Probabilidad del
> Departamento de Matemática.
>
> *PRIMERA CHARLA:*
>
> *Fecha y hora: *Miércoles 6/11/2019, 12hs
> *Lugar: *Sala de Conferencias, 2do Piso, Departamento de Matemática,
> Pabellón 1
> *Expositor: *Bernardo Nunes Borges de Lima (UFMG)
> *Título: **The Constrained-degree percolation model*
> *Abstract:* In the Constrained-degree percolation model on a graph
> $(\V,\E)$ there are a sequence, $(U_e)_{e\in\E}$, of i.i.d. random
> variables with distribution $U[0,1]$ and a positive integer $k$. Each bond
> $e$ tries to open at time $U_e$, it succeeds if both its end-vertices would
> have degrees at most $k-1$. We prove a phase transition theorem for this
> model on the square lattice $\mathbb{L}^2$, as well on the d-ary regular
> tree. We also prove that on the square lattice the infinite cluster is
> unique in the supercritical phase. Joint work with R. Sanchis, D. dos
> Santos, V. Sidoravicius and R. Teodoro.
>
>
> *SEGUNDA CHARLA:*
>
> *Fecha y hora: *Miércoles 20/11/2019, 14hs
> *Lugar: *Aula a confirmar, Pabellón 1
> *Expositor: *Martín Arjovsky (NYU)
> *Título: **Towards Learning Causal Features*
> *Abstract:* The talk will center on the deep relationship between out of
> distribution generalization, causality, and invariant correlations. From
> the study of this relationship, we introduce Invariant Risk Minimization
> (IRM), a learning paradigm to estimate invariant correlations across
> multiple training distributions. To achieve this goal, IRM learns a data
> representation such that the optimal classifier, on top of that data
> representation, matches for all training distributions. Through theory and
> experiments, we show how the invariances learned by IRM relate to the
> causal structures governing the data and enable out-of-distribution
> generalization.
>
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