Beyond the Mean: Distribution-Aware Loss Functions for Bimodal Regression

ArXi:2603.22328v1 Announce Type: cross Despite the strong predictive performance achieved by machine learning models across many application domains, assessing their trustworthiness through reliable estimates of predictive confidence remains a critical challenge. This issue arises in scenarios where the likelihood of error inferred from learned representations follows a bimodal distribution, resulting from the coexistence of confident and ambiguous predictions.