3 papers
stat.ML2024
On the good reliability of an interval-based metric to validate prediction uncertainty for machine learning regression tasks
Pascal Pernot
This short study presents an opportunistic approach to a (more) reliable validation method for prediction uncertainty average calibration. Considering that variance-based calibrati…
stat.ML2024
Negative impact of heavy-tailed uncertainty and error distributions on the reliability of calibration statistics for machine learning regression tasks
Pascal Pernot
Average calibration of the (variance-based) prediction uncertainties of machine learning regression tasks can be tested in two ways: one is to estimate the calibration error (CE) a…
stat.ML2024
Validation of ML-UQ calibration statistics using simulated reference values: a sensitivity analysis
Pascal Pernot
Some popular Machine Learning Uncertainty Quantification (ML-UQ) calibration statistics do not have predefined reference values and are mostly used in comparative studies. In conse…