[Lisa_seminaires] Fwd: Colloque du DIRO, jeudi le 18 février, 2016. Conférencier: Simon Lacoste-Julien

Yoshua Bengio yoshua.umontreal at gmail.com
Jeu 18 Fév 15:21:33 EST 2016


Simon's seminar will start in 8 minutes. It is in room 6214 (6th floor).
Please come in great numbers.
The talk is different from the one he gave in December.

-- Yoshua

---------- Forwarded message ----------
From: Neil Stewart <stewart at iro.umontreal.ca>
Date: 2016-02-13 13:22 GMT-05:00
Subject: Colloque du DIRO, jeudi le 18 février, 2016. Conférencier: Simon
Lacoste-Julien
To: seminaires at iro.umontreal.ca


 *Modern Optimization for Structured Machine Learning *

par


*Simon Lacoste-Julien*

INRIA

*Jeudi 18 février, 15:30-16:30*, *Salle 3195*, Pavillon André-Aidenstadt

    Université de Montréal, 2920 Chemin de la Tour

Café avant 15:00-15:30


*Résumé:*



Machine learning has grown significantly in the last two decades and
have had impact in diverse areas such as computer vision, natural
language processing, computational biology and social sciences. These
new applications have made apparent though that real world data have a
richer structure than have been modeled by some of the classical
paradigms of machine learning such as binary classification and
regression. In machine translation for example, the algorithm needs to
choose amongst an exponential number of possible sequences of words as
translations, and not just a few options as in handwritten digits
recognition. A key challenge in modern machine learning is to find
ways to model this complex structure in a scalable manner which is
still robust to model misspecification. In this talk, I will present
such a method that can exploit the combinatorial structure in data
represented by graphs, with various applications such as the task of
word alignment in natural language processing, the alignment of large
knowledge bases for the Semantic Web or the tracking of multiple
objects in video. I will also present how these problems have
motivated progress on novel optimization techniques including
improvements on the venerable Frank-Wolfe optimization algorithm
(1956) or Robbins-Monroe stochastic gradient method (1951). These
examples will highlight how the rich two-way street between
optimization and machine learning enables us to exploit more
effectively the structure of complex data.
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