Thodoris Lykouris

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We study the quality of outcomes in games when the population of players is dynamically changing, and where participants have to adapt to the dynamic environment. Price of Anarchy has originally been introduced to study the Nash equilibria of one-shot games. The Price of Total Anarchy extends this notion to learning outcomes in a repeated setting, assuming(More)
We show that learning algorithms satisfying a low approximate regret property experience fast convergence to approximate optimality in a large class of repeated games. Our property, which simply requires that each learner has small regret compared to a (1 + ǫ)-multiplicative approximation to the best action in hindsight, is ubiquitous among learning(More)
We propose a framework for data-driven pricing in shared vehicle systems (such as bikesharing and carsharing) in which customers can pick up and drop off vehicles in different locations. This framework provides efficient algorithms with rigorous approximation guarantees for a wide class of objective functions (including welfare and revenue), and under a(More)
We study influence maximization problems over social networks, in the presence of competition. Our focus is on diffusion processes within the family of threshold models. Motivated by the general lack of positive results establishing monotonicity and submodularity of the influence function for threshold models, we introduce a general class of(More)
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