7 citations · 15 across the 8 of their papers we have counts for
5 papers · 1 filter
Smooth ECE: Principled Reliability Diagrams via Kernel Smoothing
Jarosław Błasiok, Preetum Nakkiran
Calibration measures and reliability diagrams are two fundamental tools for measuring and interpreting the calibration of probabilistic predictors. Calibration measures quantify th…
When Does Optimizing a Proper Loss Yield Calibration?
Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu +1
Optimizing proper loss functions is popularly believed to yield predictors with good calibration properties; the intuition being that for such losses, the global optimum is to pred…
Loss Minimization Yields Multicalibration for Large Neural Networks
Jarosław Błasiok, Parikshit Gopalan, Lunjia Hu +2
Multicalibration is a notion of fairness for predictors that requires them to provide calibrated predictions across a large set of protected groups. Multicalibration is known to be…
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…
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…