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

Michael Noukhovitch mnoukhov at gmail.com
Ven 26 Jan 13:10:31 EST 2018


Reminder: this is in 20 minutes!

On Mon, Jan 22, 2018 at 3:35 PM Michael Noukhovitch <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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