Date: Wednesday, February 3rd, 2021
9:00 am – 10:00 am Pacific Time
12:00 pm – 1:00 pm Eastern Time
Location: Weekly Seminar, Zoom
We show how to achieve the notion of “multicalibration” from Hébert-Johnson et al.  not just for means, but also for variances and other higher moments. Informally, it means that we can find regression functions which, given a data point, can make point predictions not just for the expectation of its label, but for higher moments of its label distribution as well-and those predictions match the true distribution quantities when averaged not just over the population as a whole, but also when averaged over an enormous number of finely defined subgroups. It yields a principled way to estimate the uncertainty of predictions on many different subgroups-and to diagnose potential sources of unfairness in the predictive power of features across subgroups. As an application, we show that our moment estimates can be used to derive marginal prediction intervals that are simultaneously valid as averaged over all of the (sufficiently large) subgroups for which moment multicalibration has been obtained.
This talk is based on a paper that is joint work with Changhwa Lee, Mallesh M. Pai, Aaron Roth, and Rakesh Vohra.
Christopher Jung is a 4th year PhD student in the department of Computer and Information Sciences at the University of Pennsylvania, where he is fortunate to be advised by Aaron Roth and Michael Kearns. He is generally interested in algorithmic fairness, learning theory, privacy, and algorithmic game theory.