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cs.LG2022
What is Your Metric Telling You? Evaluating Classifier Calibration under Context-Specific Definitions of Reliability
John Kirchenbauer, Jacob Oaks, Eric Heim
Classifier calibration has received recent attention from the machine learning community due both to its practical utility in facilitating decision making, as well as the observati…
cs.LG2018
Exploiting Class Learnability in Noisy Data
Matthew Klawonn, Eric Heim, James Hendler
In many domains, collecting sufficient labeled training data for supervised machine learning requires easily accessible but noisy sources, such as crowdsourcing services or tagged…