Unsupervised Supervised Learning I: Estimating Classification and Regression Errors without Labels

78 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Estimating the accuracies of multiple classifiers without labeled data (2014, methodology): “This setup, denoted as unsupervisedsupervised learning in Donmez et al. [2010], appears in several other application domains, including decision science, economics and medicine, see Snow et al. [2008], Raykar et al. [2010], Parisi et al.”
Estimating Accuracy from Unlabeled Data (2014, background): “[ Donmez et al., 2010] is one of the few to estimate per-function error rates from unlabeled data.”