Ensemble-Based Dirichlet Modeling for Predictive Uncertainty and Selective Classification
Neural network classifiers trained with cross-entropy loss achieve strong predictive accuracy but lack the capability to provide inherent predictive uncertainty estimates, thus requiring external techniques to obtain these estimates. In addition, softmax scores for the true class can vary substantially across independent training runs, which limits the reliability of uncertainty-based decisions in downstream tasks. Evidential Deep Learning aims to address these limitations by producing uncertainty estimates in a single pass, but evidential training is highly sensitive to design choices including loss formulation, prior regularization, and activation functions. Therefore, this work introdu...
| Authors | Courtney Franzen, Farhad Pourkamali-Anaraki |
|---|---|
| Primary category | stat.ML |
| Published | 7 April 2026 |
| Last updated at source | 7 April 2026 |
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