4 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.LG2024
Cautious Calibration in Binary Classification
Mari-Liis Allikivi, Joonas Järve, Meelis Kull
Being cautious is crucial for enhancing the trustworthiness of machine learning systems integrated into decision-making pipelines. Although calibrated probabilities help in optimal…
cs.LG2021★ 4 cited
Shift Happens: Adjusting Classifiers
Theodore James Thibault Heiser, Mari-Liis Allikivi, Meelis Kull
Minimizing expected loss measured by a proper scoring rule, such as Brier score or log-loss (cross-entropy), is a common objective while training a probabilistic classifier. If the…