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Richard Mudd

2 accepted papers

2023

Explaining Predictive Uncertainty with Information Theoretic Shapley Values

NeurIPS 2023poster

Researchers in explainable artificial intelligence have developed numerous methods for helping users understand the predictions of complex supervised learning models. By contrast, explaining the $\textit{uncertainty}$ of model outputs has received relatively little attention. We adapt the popular Sh…

2023

TCE: A Test-Based Approach to Measuring Calibration Error

UAI 2023poster

This paper proposes a new metric to measure the calibration error of probabilistic binary classifiers, called test-based calibration error (TCE). TCE incorporates a novel loss function based on a statistical test to examine the extent to which model predictions differ from probabilities estimated fr…