The bandits with knapsack (BwK) framework models online decision-making
...
We consider the problem of steering no-regret-learning agents to play
de...
We introduce a new approach for computing optimal equilibria via learnin...
Bayesian persuasion studies how an informed sender should influence beli...
Online advertising platforms typically use auction mechanisms to allocat...
We study fully dynamic online selection problems in an adversarial/stoch...
We study online learning problems in which a decision maker has to take ...
We study the problem of finding optimal correlated equilibria of various...
We study online learning problems in which a decision maker wants to max...
A recent emerging trend in the literature on learning in games has been
...
While in two-player zero-sum games the Nash equilibrium is a well-establ...
In this work we investigate the strategic learning implications of the
d...
Budget-management systems are one of the key components of modern auctio...
Bayesian persuasion studies how an informed sender should partially disc...
The existence of simple uncoupled no-regret learning dynamics that conve...
We focus on the problem of finding an optimal strategy for a team of two...
The existence of simple, uncoupled no-regret dynamics that converge to
c...
Recently, there has been growing interest around less-restrictive soluti...
Network congestion games are a well-understood model of multi-agent stra...
Persuasion studies how an informed principal may influence the behavior ...
Many real-world applications involve teams of agents that have to coordi...
In the context of multi-player, general-sum games, there is an increasin...
We focus on the following natural question: is it possible to influence ...
We study an information-structure design problem (a.k.a. persuasion) wit...
We investigate the computation of equilibria in extensive-form games whe...
We provide, to the best of our knowledge, the first computational study ...
The Team-maxmin equilibrium prescribes the optimal strategies for a team...