Agenda

Speaker: Nicolò Cesa-Bianchi, University of Milan

Abstract
Experts and bandits are machine learning paradigms using the theory of repeated games to model a learning agent interacting with an ever-changing environment. In this talk, we show a simple and unified analysis of the main algorithms for these prediction problems. Next, motivated by applications in the context of recommender systems, we study specific examples of interaction between agent and environment. We then focus on a concrete application, showing how the bandit paradigm can be applied to maximize the seller's revenue in online advertising.

Bio sketch
Nicolò Cesa-Bianchi is professor of Computer Science at the University of Milano. He was President of the Association for Computational Learning and member of the steering committee of the EC-funded Network of Excellence PASCAL2. He held visiting positions with UC Santa Cruz, Graz Technical University, Ecole Normale Superieure in Paris, Google, and Microsoft Research. He received a Google Research Award (2010) and a Xerox University Affairs Committee Award (2011-2014). His research interests focus on: theory and applications of machine learning, sequential optimization, and algorithmic game theory. On these topics, he published two monographs: "Prediction, Learning, and Games" (Cambridge University Press, 2006) and "Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems" (NOW Publishers, 2012). He has over 150 indexed publications, including more than 40 papers in international scholarly journals. Overall, his papers have been cited over 10000 times