Dear all, I would like to advertise the following seminar, that might be of interest to some of you.

Best, -- Leonardo.

-------- Messaggio originale -------- Oggetto: [Dipartimento.di] Seminar: Machine Learning & Optimization: A Fruitful Interplay - M. Sanguineti Data: 06-03-2019 16:28 Mittente: Alessio Micheli micheli@di.unipi.it Destinatario: dipartimento@di.unipi.it

Cari tutti, con piacere annuncio il seguente seminario di Marcello Sanguineti in dipartimento.

DATE/HOUR: Thursday, March 28, 2019 – 3 P.M.

ROOM: Sala seminari ovest - Dip. di Informatica – Pisa

TITLE: Machine Learning & Optimization: A Fruitful Interplay

SPEAKER: Marcello Sanguineti, Department of Computer Science, Bioengineering,Robotics, and Systems Engineering (DIBRIS), University of Genova

ABSTRACT: Since the very beginning, there has been a fruitful exchange between machine learning and optimization. While machine learning exploits optimization models and algorithms, it simultaneously poses problems which often constitute optimization challenges. This cross-fertilization is particularly evident nowadays. Many applications produce data that have to be processed and many other applications are based on data that have already been processed. In society, data-producers and data-consumers continuously exchange their roles. At the same time, scientists generate and collect huge amounts of data and need to develop methodologies for data analysis. This requires the use of ever more powerful approaches and techniques. Among these improvements, those coming from the field of optimization surely play a basic role. Not only classical optimization frameworks, such as stochastic approximation, first-order methods, and convex relaxation, are exploited in machine learning, but also more sophisticated models and techniques, such as regularized optimization, robust optimization, and functional optimization. In this talk, I provide a non-technical review of some optimization paradigms and techniques used in machine learning and I emphasize some aspects at the confluence of these two disciplines. The interplay between machine learning and optimization continues to develop, and its benefits are there for all to see.

Riferimento: Alessio Micheli.