Post-hoc loss-calibration for bayesian neural networks

Abstract

A computing device and computer-implemented method for post-hoc correction of a decision generated by a machine learning model. The computing device accesses a trained first machine learning (ML) model, a dataset, and a utility function. The computing device trains a second ML model based on performing post-hoc correction of a first set of decisions generated by the first ML model on the dataset. The training includes processing the first set of decisions with respect to a second set of decisions made by the second ML model on the dataset. The training further includes configuring, based on the processing, the second ML model with parameters from a set of parameters optimizing a loss-objective function that concurrently maximizes utility of the second set of decisions according to the utility function and a log-likelihood on the dataset. After training, the second ML model is outputted as a loss-calibrated ML model.