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.
Afficher les réponses par date
Reminder: this is in 20 minutes!
On Mon, Jan 22, 2018 at 3:35 PM Michael Noukhovitch mnoukhov@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.
lisa_seminaires@iro.umontreal.ca
-
Michael Noukhovitch