[Lisa_seminaires] [Visitor Talk] Gael Varoquaux, Friday 2pm

Pascal Vincent pascal20100 at gmail.com
Mer 22 Aou 15:31:30 EDT 2018


Hello all, this Friday, we will have the visit of *Gaël Varoquaux* from
INRIA, who will be giving a talk at 2 p.m. in AA-3195.

Gaël is famous for creating the highly successful scikit-learn software and
for his past work on brain imaging and neuroscience. But he will be talking
primarily about his current research interests, which are machine learning
approaches applicable to more general types of data.

Xavier Bouthillier is coordinating his Friday visit, please get in touch
with him (xavier.bouthillier at gmail.com) if you want to set aside time to
meet the speaker.

*When:* this Friday, august 24th, at 2 p.m.

*Where:* Room 3195, Pavillon André Aisenstadt

*Title:* Simple representations for learning: factorizations and
similarities

*Abstract*

Real-life data seldom comes in the ideal form for statistical learning.
This talk will focus on high-dimensional problems for signals and
discrete entities: when dealing with many, correlated, signals or
entities, it is useful to extract representations that capture these
correlations.

Matrix factorization models provide simple but powerful representations.
They are used for recommender systems across discrete entities such as
users and products, or to learn good dictionaries to represent images.
However they entail large computing costs on very high-dimensional data,
databases with many products or high-resolution images. I will present an
algorithm to factorize huge matrices based on stochastic subsampling that
gives up to 10-fold speed-ups [1].

With discrete entities, the explosion of dimensionality may be due to
variations in how a smaller number of categories are represented. Such a
problem of "dirty categories" is typical of uncurated data sources. I
will discuss how encoding this data based on similarities recovers a
useful category structure with no preprocessing. I will show how it
interpolates between one-hot encoding and techniques used in
character-level natural language processing.


[1] Stochastic subsampling for factorizing huge matrices
A Mensch, J Mairal, B Thirion, G Varoquaux
IEEE Transactions on Signal Processing 66 (1), 113-128

[2] Similarity encoding for learning with dirty categorical variables. P
Cerda, G Varoquaux, B Kégl Machine Learning (2018): 1-18
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