We study the power of cooperation in a network of communicating agents that solve a learning task. Agents use an underlying communication network to get information about what the other agents know. In the talk, we show the extent to which cooperation allows to prove performance bounds that are strictly better than the known bounds for non-cooperating agents. Our results are formulated within the online learning setting, under both the full and partial feedback models.
Nicolò Cesa-Bianchi is 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. 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, including NeurIPS, COLT, and ALT. 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 and co-director of the ELLIS program on Interactive Learning and Interventional Representations. He serves on the steering committees of the Italian Laboratory on AI and Intelligent Systems, and of the Italian PhD program on AI.
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