[Lisa_seminaires] 3 talks this week

Yoshua Bengio yoshua.umontreal at gmail.com
Sam 13 Fév 14:43:34 EST 2016


Hi all,

There are three talks to attend this week:

Tuesday 10h30: Mingbin Feng, on reusing outputs of simulation experiments
Thursday 15h30: Simon Lacoste-Julien, on structured machine learning
Friday 14h30: Yoshua Bengio, on biologically plausible backprop

Nota-Bene: a TV crew should be around between Wednesday and Friday.

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 *Green Simulation: Reusing the Output of Simulation Experiment*

par


*Mingbin Feng*

 Northwestern University

*Mardi 16 février, 10:30-11:30*, *Salle 3195*, Pavillon André-Aidenstadt

    Université de Montréal, 2920 Chemin de la Tour

Café avant 10:00-10:30
 *Résumé:*

In finance and insurance, simulations are often run repeatedly with
different inputs. For example, in a simulation for risk management,
the simulation model for valuing derivative securities is run many
times with different macroeconomic conditions. We present a new
concept of green simulation, which seeks to increase the computational
efficiency of the current experiment by reusing simulation output
generated during previous experiments. Green simulation views
simulation output as scarce resource and turns the computational
expense in an experiment into computation investment for future ones.
We propose and examine two green simulation estimators for repeated
experiments whose inputs are observations from an underlying
stochastic process. Two types of convergence are shown for these green
simulation estimators under different ass
umptions. As illustrated by two practical applications: catastrophe
bond pricing and periodic credit risk evaluation, green simulation is
both theoretically sound and practically useful.


---------- 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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