Online learning, bandits, and digital markets

Part of the Special ECE Seminar Series 

Modern Artificial Intelligence

Title:

Online learning, bandits, and digital markets

Speaker:

Nicolò Cesa-Bianchi, University of Milan and Polytechnic University of Milan, Italy

Abstract:

Online learning is concerned with the study of algorithms that learn sequentially through repeated interactions with an unknown environment. The goal is to understand how fast an agent can learn depending on the information received from the environment. Digital markets, with their complex ecosystems of algorithmic agents, provide countless examples of sequential decision-making problems with different utility functions and types of learning feedback. In the talk, after tracing the roots and the main algorithmic ideas behind online learning, we will show how solving problems arising from digital markets has improved our understanding of what machine learning algorithms can do.

Bio:

Nicolò Cesa-Bianchi is professor of Computer Science at the University of Milan (Italy), where he leads the laboratory of artificial intelligence and learning algorithms. He also holds a joint appointment at Polytechnic University of Milan (Italy). His main research interests are the design and analysis of machine learning algorithms for online learning, sequential decision-making, and graph analytics. 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 committees of some of the most important machine learning conferences. He is the recipient of a Google Research Award, a Xerox Foundation Award, a Criteo Faculty Award, a Google Focused Award, and an IBM Research Award. He is ELLIS fellow, member of the ELLIS board, and co-director of the Milan ELLIS unit.