14 citations · 16 across the 7 of their papers we have counts for
3 papers · 1 filter
Active learning with biased non-response to label requests
Thomas Robinson, Niek Tax, Richard Mudd +1
Active learning can improve the efficiency of training prediction models by identifying the most informative new labels to acquire. However, non-response to label requests can impa…
TCE: A Test-Based Approach to Measuring Calibration Error
Takuo Matsubara, Niek Tax, Richard Mudd +1
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 func…
Explaining Predictive Uncertainty with Information Theoretic Shapley Values
David S. Watson, Joshua O'Hara, Niek Tax +2
Researchers in explainable artificial intelligence have developed numerous methods for helping users understand the predictions of complex supervised learning models. By contrast,…