[DL] Spotlight Seminar on AI - NICOLO’ CESA-BIANCHI - June 22

[Chiara Ghidini]

[* Apologies in case of multiple posting *]

The Italian Association for Artificial Intelligence is pleased to announce the next seminar of its Spotlight Seminars on AI initiative:

June, 22– 5:00PM (CEST)
Title: Online learning, bandits, and digital markets
Speaker: NICOLO’ CESA-BIANCHI, University of Milan and Polytechnic University of Milan

The aim of the seminar series is to illustrate, explore and discuss current scientific challenges, trends, and possibilities in all branches of our articulated research field. The seminars will be held virtually on the YouTube channel of the Association (https://www.youtube.com/c/AIxIAit), on a monthly basis (and made permanently available on that channel), by leading Italian researchers as well as by top international scientists. 

The seminars are mainly aimed at a broad audience interested in AI research, and they are also included in the Italian PhD programme in Artificial Intelligence; indeed, AIxIA warmly encourages the attendance of young scientists and PhD students. 

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Bio: Nicolò Cesa-Bianchi is professor of Computer Science at the University of Milan and 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.

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.

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The Spotlight Seminars on AI Committee, 
Giuseppe De Giacomo
Chiara Ghidini
Gianluigi Greco
Marco Maratea
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