3 papers
stat.ML2022
Net benefit, calibration, threshold selection, and training objectives for algorithmic fairness in healthcare
Stephen R. Pfohl, Yizhe Xu, Agata Foryciarz +3
A growing body of work uses the paradigm of algorithmic fairness to frame the development of techniques to anticipate and proactively mitigate the introduction or exacerbation of h…
stat.ME2020
-Ridge: group regularized ridge regression via empirical Bayes noise level cross-validation
Nikolaos Ignatiadis, Panagiotis Lolas
Features in predictive models are not exchangeable, yet common supervised models treat them as such. Here we study ridge regression when the analyst can partition the features into…
stat.ME2019
Covariate-Powered Empirical Bayes Estimation
Nikolaos Ignatiadis, Stefan Wager
We study methods for simultaneous analysis of many noisy experiments in the presence of rich covariate information. The goal of the analyst is to optimally estimate the true effect…