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 consider a platform's problem of collecting data from privacy sensitive users to estimate
an underlying parameter of interest. We formulate this question as a Bayesian-optimal …

Stackelberg games are a canonical model for strategic principal-agent interactions.
Consider, for instance, a defense system that distributes its security resources across high …

Organizations that would mutually benefit from pooling their data are otherwise wary of
sharing. This is because sharing data is costly-in time and effort-and, at the same time, the …

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 …

Understanding the convergence properties of learning dynamics in repeated auctions is a
timely and important question in the area of learning in auctions, with numerous applications …

The growing adoption of data analytics platforms and machine learning-based solutions for
decision-makers creates a significant demand for datasets, which explains the appearance …

We study learning algorithms that optimize revenue in repeated contextual posted-price
auctions where a seller interacts with a single strategic buyer that seeks to maximize his …

We study the design of optimal Bayesian data acquisition mechanisms for a platform
interested in estimating the mean of a distribution by collecting data from privacy-conscious …

We study a multi-round mechanism design problem, where we interact with a set of agents
over a sequence of rounds. We wish to design an incentive-compatible (IC) online learning …