18 citations · 21 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
Can bin-wise scaling improve consistency and adaptivity of prediction uncertainty for machine learning regression ?
Pascal Pernot
Binwise Variance Scaling (BVS) has recently been proposed as a post hoc recalibration method for prediction uncertainties of machine learning regression problems that is able of mo…
Calibration in Machine Learning Uncertainty Quantification: beyond consistency to target adaptivity
Pascal Pernot
Reliable uncertainty quantification (UQ) in machine learning (ML) regression tasks is becoming the focus of many studies in materials and chemical science. It is now well understoo…
Scaling factors for ab initio vibrational frequencies: comparison of uncertainty models for quantified prediction
Pascal Pernot
Bayesian Model Calibration is used to revisit the problem of scaling factor calibration for semi-empirical correction of ab initio calculations. A particular attention is devoted t…