Accurate Uncertainties for Deep Learning Using Calibrated Regression

Volodymyr Kuleshov, Nathan Fenner, Stefano Ermon

Abstract: Methods for reasoning under uncertainty are a key building block of accurate and reliable machine learning systems. Bayesian methods provide a general framework to quantify uncertainty. However, be...