June 2016 - Lisa_seminaires - mailman.iro.umontreal.ca

Microsoft visit + presentation on dialogue tomorrow by Julian Serban, 10h30am, 3195
by Yoshua Bengio 29 Jun '16

29 Jun '16

Hello, Tomorrow morning we have a visit by a top brass from microsoft, Executive Vice-President Qi Lu. (1) please be at the lab at 10am, we want to show our "strength in numbers" (they arrive at my office at 10am) (2) please attend a presentation that Julian Serban will give to our visitors, at 10h30 in rm 3195, on Building Dialogue Systems Using Neural Networks Thank you, -- Yoshua

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Re: [Lisa_seminaires] Pr?doc oral de Guillaume Alain: lundi 27 juin
by Aaron Courville 27 Jun '16

27 Jun '16

Reminder: Today is Guillaume Alain's predoc oral, at 11h00 in AA3195 Titre: "Denoising autoencoders, importance sampling, and visualizing training with probes" FYI: I understand the talk will be in French. Président-rapporteur: Aaron Courville Membre: Laurent Charlin Directeur de Recherche: Yoshua Bengio -- Aaron 2016-06-24 9:21 GMT-04:00 Aaron Courville <aaron.courville(a)gmail.com>: > Bonjour, > vous êtes invité au prédoc oral de Guillaume Alain. > Lundi 27 juin à 11h00 au 3195. > > Titre: "Denoising autoencoders, importance sampling, and visualizing > training with probes" > > Président-rapporteur: Aaron Courville > Membre: Laurent Charlin > Directeur de Recherche: Yoshua Bengio > > -- Aaron > > -- > Aaron C. Courville > Département d’Informatique et > de recherche opérationnelle > Université de Montréal > email:Aaron.Courville@gmail.com > -- Aaron C. Courville Département d’Informatique et de recherche opérationnelle Université de Montréal email:Aaron.Courville@gmail.com

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Fwd: a talk by Valentine Kabanets
by Yoshua Bengio 24 Jun '16

24 Jun '16

---------- Forwarded message ---------- From: Pierre McKenzie <mckenzie(a)iro.umontreal.ca> Date: 2016-06-24 9:42 GMT-04:00 Subject: a talk by Valentine Kabanets To: Yoshua Bengio <bengioy(a)iro.umontreal.ca>, Pascal Vincent < vincentp(a)iro.umontreal.ca>, christopher.pal(a)polymtl.ca, Aaron Courville < aaron.courville(a)umontreal.ca>, roland.memisevic(a)umontreal.ca, lcharlin(a)gmail.com Cc: Sylvie Hamel <hamelsyl(a)iro.umontreal.ca> Bonjour collègues du MILA, If you think some members of your lab might be interested, please don't hesitate to distribute the announcement. Regards, Pierre McKenzie ------------------------------ Tuesday June 28 @ 11:00 in room 3195, pavillon André-Aisenstadt of U de M Speaker: Valentine Kabanets (Simon Fraser) Title: Learning Algorithms from Natural Proofs Abstract: Based on Hastad's (1986) circuit lower bounds, Linial, Mansour, and Nisan (J ACM, 1993) gave a quasipolytime learning algorithm for AC^0 (constant-depth circuits with AND, OR, and NOT gates), in the PAC model over the uniform distribution. It was an open question to get a learning algorithm (of any kind) for the class of AC^0[p] circuits (constant-depth, with AND, OR, NOT, and mod-p gates for a prime p). Our main result is a quasipolytime learning algorithm for AC^0[p] in the PAC model over the uniform distribution with membership queries. This algorithm is an application of the general connection between natural properties (in the sense of Razborov and Rudich (JCSS, 1997)) and learning algorithms that we obtain. We show that a natural circuit lower bound against any (sufficiently powerful) circuit class yields a learning algorithm for the same circuit class. Then the known lower bounds against AC ^0[p] by Razborov (1987) and Smolensky (1987), shown to be natural by Razborov and Rudich, imply our main result. Joint work with Marco Carmosino (UCSD), Russell Impagliazzo (UCSD), and Antonina Kolokolova (MUN).

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ML talk tomorrow by François Laviolette
by Yoshua Bengio 14 Jun '16

14 Jun '16

Speaker: François Laviolette Wednesday (15th June) at 11h00. Room: TBA *1.**- TITLE: **Sparsity, interpretability and sample compression in Machine Learning* *2.- ABSTRACT:* We will investigate the Machine Learning subject called the Fat Data paradigm, a setting where the dimension of the feature space is much bigger than the number of training examples. This is a situation often encountered in data arising from life science. In such situations, there is a real risk of overfitting, even if one makes use of highly regularized learning algorithms. To overcome the lack of examples and even achieve good generalization performances, one solution consists in including prior knowledge of the domain into the learning algorithm. Another approach is to look for predictors that will be interpretable by experts in the domain, who in turn will help to validate the prediction. The Set Covering Machine, introduced by Mario Marchand and John Shawe-Taylor more than a decade ago, is a learning algorithm that can be used to produce such interpretable predictors. We will present the original version of this algorithm together with a new one, specialized for genomic data. We will also show some results, based on the sample compression theory, that show why such a learning algorithm can alleviate overfitting, even in the fat data situation. *3.- Bio Sketch* François Laviolette is a full Professor at the department of Computer Science and Software Engineering of Laval University. He received his doctorate in graph theory at the University of Montreal in 1995. His thesis solved an old problem that had been studied among others by the mathematician Paul Erdos. For over 10 years, his main area of research has been Machine Learning. More specifically, he develops learning algorithms to solve new types of learning problems, including problems relating to genomics and proteomics. He is currently the director of the new Big Data Research Center at Laval University.

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