Abstract Multi-distribution learning (MDL), which seeks to learn a shared model that
minimizes the worst-case risk across $ k $ distinct data distributions, has emerged as a …

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 …

Rankings are ubiquitous across many applications, from search engines to hiring
committees. In practice, many rankings are derived from the output of predictors. However …

The goal of multi-objective optimization (MOO) is to learn under multiple, potentially
conflicting, objectives. One widely used technique to tackle MOO is through linear …

A canonical desideratum for prediction problems is that performance guarantees should
hold not just on average over the population, but also for meaningful subpopulations within …

Calibration is a well-studied property of predictors which guarantees meaningful uncertainty
estimates. Multicalibration is a related notion--originating in algorithmic fairness--which …

This paper examines the issue of fairness in the estimation of graphical models (GMs),
particularly Gaussian, Covariance, and Ising models. These models play a vital role in …

We study the problem of online multi-group learning, a learning model in which an online
learner must simultaneously achieve small prediction regret on a large collection of …

Gradient-based algorithms have shown great promise in solving large (two-player) zero-sum
games. However, their success has been mostly confined to the low-precision regime since …

We initiate the study of the truthfulness of calibration measures in sequential prediction. A
calibration measure is said to be truthful if the forecaster (approximately) minimizes the …