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cs.LG2022★ 2 cited
A Unifying Theory of Distance from Calibration
Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu +1
We study the fundamental question of how to define and measure the distance from calibration for probabilistic predictors. While the notion of perfect calibration is well-understoo…
cs.LG2022★ 1 cited
What You See is What You Get: Principled Deep Learning via Distributional Generalization
Bogdan Kulynych, Yao-Yuan Yang, Yaodong Yu +2
Having similar behavior at training time and test time what we call a "What You See Is What You Get" (WYSIWYG) property is desirable in machine learning. Models trained wit…