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Introduces ELVAE, a VAE with evidential learning that models latent coordinates with normal-inverse-gamma posteriors to obtain explicit uncertainty estimates. Experiments on MNIST show that within-class uncertainty ranking can stratify synthetic sample reliability and stress-testing, though results require class-wise normalization and vary across seeds.
Introduces Fast Evidential Rule Learning (FERL), a method for interpretable classification that produces evidential outputs and can abstain when uncertain, with theoretical stability guarantees and strong empirical results across tabular and concept-bottleneck benchmarks.
This paper introduces Evidential Adversarial Training (EV-AT), a method that improves the robustness-uncertainty trade-off in classifiers by combining an evidence-based loss with robust evidence alignment, achieving state-of-the-art results on selective classification benchmarks.