Machine Learning and Sequential Decision Making

Seminar / Lecture
Open to the Public

Geometric abstract image of brain


Part of the Special ECE Seminar Series 

Modern Artificial Intelligence


Machine Learning and Sequential Decision Making


Nicolò Cesa-Bianchi, Università degli Studi di Milano, Italy


A solid theoretical understanding of the algorithms that power machine learning systems is of increasing importance given the pervasiveness of AI technologies. In online learning, a setting in which agents make repeated decisions on a stream of data, the predictive performance of an algorithm can be certified through surprisingly robust mathematical guarantees. The talk will focus on learning with partial feedback, a framework that is successfully applied to many domains including product recommendation and online advertising. With the help of concrete examples, we will explore the extent to which different forms of partial feedback, obtained through observation or communication with other agents, can affect the learning ability of online algorithms.


Nicolò Cesa-BianchiNicolò Cesa-Bianchi is a professor of Computer Science at the University of Milan, Italy. His main research interests are the design and analysis of machine learning algorithms for statistical and online learning, multi-armed bandit problems, and graph analytics. On these topics, he has published over 140 papers. He is co-author of the monographs "Prediction, Learning, and Games" and "Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems". He served as President of the Association for Computational Learning and co-chaired the program committee of some of the most important machine learning conferences, including NeurIPS and COLT. He is the recipient of a Google Research Award, a Xerox Foundation Award, a Criteo Faculty Award, and a Google Focused Award.