Place: Seminari Room (1st floor)
Affiliation: KIIS Research Center at Università Ca’ Foscari Venezia, Italy.
The main point made by game theorists is to shift the emphasis from optimality criteria to equilibrium conditions. As it provides an abstract theoretically-founded framework to elegantly model complex scenarios, game theory has found a variety of applications not only in economics and, more generally, social sciences but also in different ﬁelds of engineering and information technologies. In particular, in the past there have been various attempts aimed at formulating problems in computer vision, pattern recognition and machine learning from a game-theoretic perspective and, with the recent development of algorithmic game theory, the interest in these communities around game-theoretic models and algorithms is growing at a fast pace. The goal of this seminar is to describe our recent efforts to foster the adoption of such methods to solve a wide range of computer vision problems, especially when dealing with tasks involving the discovery of sparse groups of inliers within a mostly noisy dataset. We shall assume no pre-existing knowledge of game theory by the audience, thereby making the tutorial self-contained and understandable by a non-expert.