We propose a model for learning with bandit feedback while accounting fo...
Algorithmic decision-making in high-stakes domains often involves assign...
In this paper, we introduce a generalization of the standard Stackelberg...
Recommendation systems are pervasive in the digital economy. An importan...
We propose a framework for decision-making in the presence of strategic
...
We study the problem of contextual search in the adversarial noise model...
We study the effects of information discrepancy across sub-populations o...
Lipschitz bandits is a prominent version of multi-armed bandits that stu...
Standard game-theoretic formulations for settings like contextual pricin...
We study online learning settings in which experts act strategically to
...
We study the problem of online learning in strategic classification sett...
We study incentive compatible mechanisms for Combinatorial Auctions wher...
This paper is part of an emerging line of work at the intersection of ma...
We address online learning in complex auction settings, such as sponsore...