9 citations · 20 across the 9 of their papers we have counts for
Showing 2021Show all
2 papers · 1 filter
stat.ML2021★ 2 cited
Tracking the risk of a deployed model and detecting harmful distribution shifts
Aleksandr Podkopaev, Aaditya Ramdas
When deployed in the real world, machine learning models inevitably encounter changes in the data distribution, and certain -- but not all -- distribution shifts could result in si…
stat.ML2021★ 7 cited
Distribution-free uncertainty quantification for classification under label shift
Aleksandr Podkopaev, Aaditya Ramdas
Trustworthy deployment of ML models requires a proper measure of uncertainty, especially in safety-critical applications. We focus on uncertainty quantification (UQ) for classifica…