[Todos CMAT] Fwd: Charla sobre Clustering - M. Tepper - **lunes 20/11-16hs**

Diego Armentano diego en cmat.edu.uy
Lun Nov 20 13:58:24 -03 2017

---------- Forwarded message ---------
From: Mauricio Delbracio <mdelbra en fing.edu.uy>
Date: vie., 17 nov. 2017 18:18
Subject: Re: Charla sobre Clustering - M. Tepper - **lunes 20/11-16hs**
To: todos iie <todos_iie en fing.edu.uy>, Marcelo Fiori - IIE - IMERL <
mfiori en fing.edu.uy>, Santiago Castro - InCo <sacastro en fing.edu.uy>, Hector
Cancela - INCO <cancela en fing.edu.uy>, Matias Di Martino <
matiasdm en fing.edu.uy>, Diego Armentano <diego en cmat.edu.uy>, Guillermo
Moncecchi <gmonce en fing.edu.uy>, Paola Bermolen <paola en fing.edu.uy>, lorena
etcheverry <lorenae en fing.edu.uy>, Diego Vallespir <dvallesp en fing.edu.uy>,
Ernesto Mordecki <mordecki en cmat.edu.uy>, Andrés Sosa <asosa en cmat.edu.uy>
Cc: German Capdehourat <gercap en gmail.com>, Cecilia Aguerrebere <
caguerrebere en gmail.com>


Les recuerdo la charla de Mariano Tepper: lunes 20/11 a las 16hs en
salón de seminarios de FÍSICA (fing)


> ---------- Forwarded message ----------
> Tenemos el agrado de tener de visita a Mariano Tepper, investigador
> del grupo de neurociencia del Flatiron Institute (Simons Foundation) y
> amigo de la casa.
> Mariano dará una charla sobre su trabajo actual, que muchos de ustedes
> (espero) encontrarán de interés.
> "Clustering is semidefinitely not that hard",  lunes 20/11 a las 16hs.
> Sala de seminarios del Instituto de Física (7mo piso FING)
> Abajo +información. Favor reenviar a interesados.
> saludos,
> mauricio
> ---
> Title:
> Clustering is semidefinitely not that hard
> Abstract:
> In recent years, semidefinite programs (SDP) have been the subject of
> interesting research in the field of clustering. In many cases, these
> convex programs deliver the same answers as non-convex alternatives
> and come with a guarantee of optimality. In this talk, I will argue
> that SDP-KM, a popular semidefinite relaxation of K-means, can learn
> manifolds present in the data, something not possible with the
> original K-means formulation. To build an intuitive understanding of
> SDP-KM's manifold learning capabilities, I will present a theoretical
> analysis on an idealized dataset. Additionally, SDP-KM even segregates
> linearly non-separable manifolds. As generic SDP solvers are slow on
> large datasets, I will also discuss the suitability of efficient
> algorithms to SDP-KM. These features render SDP-KM a versatile and
> interesting tool for manifold learning while remaining amenable to
> theoretical analysis.
> Bio:
> Mariano Tepper is currently a member of the neuroscience group at the
> Center for Computational Biology, Flatiron Institute. His research
> focuses on image processing, computer vision, pattern recognition, and
> machine learning. Previously, he was a research scientist at Duke
> University. Prior to working at Duke, he was postdoctoral research
> associate at the University of Minnesota. Tepper holds a Ph.D. and
> licentiate degree in computer science from the Universidad de Buenos
> Aires in Argentina and an M.S. in applied mathematics from the École
> Normale Supérieure de Cachan in France.
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