In this paper, we introduce a generalization of the standard Stackelberg Games (SGs)
framework: Calibrated Stackelberg Games. In CSGs, a principal repeatedly interacts with an …

We study a repeated Principal Agent problem between a long lived Principal and Agent pair
in a prior free setting. In our setting, the sequence of realized states of nature may be …

We study a ubiquitous learning challenge in online principal-agent problems during which
the principal learns the agent's private information from the agent's revealed preferences in …

Algorithmic decision-making in high-stakes domains often involves assigning decisions to
agents with incentives to strategically modify their input to the algorithm. In addition to …

We study the incentivized information acquisition problem, where a principal hires an agent
to gather information on her behalf. Such a problem is modeled as a Stackelberg game …

We study the problem of characterizing optimal learning algorithms for playing repeated
games against an adversary with unknown payoffs. In this problem, the first player (called …

We study the fundamental mistake bound and sample complexity in the strategic
classification, where agents can strategically manipulate their feature vector up to an extent …

Many human-facing algorithms--including those that power recommender systems or hiring
decision tools--are trained on data provided by their users. The developers of these …

We present a study on a repeated delegated choice problem, which is the first to consider an
online learning variant of Kleinberg and Kleinberg, EC'18. In this model, a principal interacts …

A common explanation for negative user impacts of content recommender systems is
misalignment between the platform's objective and user welfare. In this work, we show that …