Jeudi 3 juillet 2014 de 14 h à 15 h 30
Auditorium IRCICA - Villeneuve d’Ascq
Online learning has become a standard tool in machine learning and large-scale data analysis. Learning is viewed as a repeated game between an adaptive agent and an ever-changing environment. Within this simple paradigm, one can model a variety of sequential decision tasks simply by specifying the interaction protocol and the resource constraints on the agent. In the talk we will describe algorithmic applications to specific learning scenarios (partial feedback, attribute-efficient, multitask, semi-supervised, and more). In order to provide a specific example of online learning, in the final part of the talk we will focus on the experts/bandits model and show a simple algorithmic setting that generalizes previous and seemingly different approaches.