[Lisa_teatalk] FW: [Tea Talk] Bart Van Parys (MIT) Fri Jan 26 1:30PM AA1360

Daniel Doyon daniel.o.doyon at gmail.com
Mon Jan 29 11:16:42 EST 2018


Hi, 

 

I unfortunately missed  Bart Van Parys’s Tea Talk last Friday, Jan26. I was curious if his presentation could be forwarded my way to get a flavor of the talk!

 

Thanks, 

 

Dan

 

From: lisa_teatalk-bounces at iro.umontreal.ca <mailto:lisa_teatalk-bounces at iro.umontreal.ca>  [mailto:lisa_teatalk-bounces at iro.umontreal.ca] On Behalf Of Michael Noukhovitch
Sent: Friday, January 26, 2018 1:11 PM
To: Tea Talks MILA <teatalk-orgs at iro.umontreal.ca <mailto:teatalk-orgs at iro.umontreal.ca> >; lisa_teatalk at iro.umontreal.ca <mailto:lisa_teatalk at iro.umontreal.ca> ; lisa_seminaires at iro.umontreal.ca <mailto:lisa_seminaires at iro.umontreal.ca> ; Lisa Labo <lisa_labo at iro.umontreal.ca <mailto:lisa_labo at iro.umontreal.ca> >; lisa_montreal at iro.umontreal.ca <mailto:lisa_montreal at iro.umontreal.ca> 
Subject: Re: [Lisa_teatalk] [Tea Talk] Bart Van Parys (MIT) Fri Jan 26 1:30PM AA1360

 

Reminder: this is in 20 minutes!

 

On Mon, Jan 22, 2018 at 3:35 PM Michael Noukhovitch <mnoukhov at gmail.com <mailto:mnoukhov at gmail.com> > wrote:

Hi all!

 

We will have a researcher from MIT, Bart Van Parys, giving a talk on Friday Jan 26th at 1:30PM in room AA1360.  

 

Note the later time, 1:30!

 

This is a seminar is organized in collaboration with the Canada Research Chair in Decision Making under Uncertainty at GERAD! So this is ideal for all the optimization fans out there!

Based on my experience, this will be a great talk and coming to it should be an easy decision!

Michael

KEYWORDS Optimization, Sparse Regression, Generalization


TITLE Modern Optimization for Sparse Learning and Robust Analytics

 

ABSTRACT 

We discuss the tremendous potential of integer optimization
methods for learning predictive models from high-dimensional data via
exact sparse regression. We show that novel integer formulations can
solve exact sparse regression problems of sizes counting p=100,000s
covariates for n=10,000s of samples. That is, two orders of magnitude
more than current state of the art methods. We also indicate that robust
optimization methods can help practitioners make data-driven decisions
which are safeguarded against over-calibration to one particular data
set. We claim that robust optimization methods have an enormous untapped
potential when making subsequent decisions based on data.

 

BIO 
Bart Van Parys is currently a postdoctoral researcher working
with Prof. Dimitris Bertsimas at the MIT Sloan School of Management. His
research interests are situated on the interface between optimization
and machine learning. In 2015 he obtained his Ph.D. in control theory at
the Swiss Federal Institute of Technology (ETH) in Zurich under the
supervision of Prof. Manfred Morari. He received his M.E. from the
University of Leuven in 2011.

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