BOLT: Universal Classifier Training via a Bayes-Error Upper Bound

Name: BOLT: Universal Classifier Training via a Bayes-Error Upper Bound
Uploaded: 2026-10-17T11:40:31.015196+00:00
Duration: 403 s
Description: The work introduces a novel upper bound on Bayes error using f-divergence to create a new loss function called Bayes Optimal Learning Threshold (BOLT). This loss trains classifiers by minimizing an expected term that upper-bounds the true Bayes error rate. Experiments show BOLT training often achieves lower held-out classification errors than standard cross-entropy training.

The work introduces a novel upper bound on Bayes error using f-divergence to create a new loss function called Bayes Optimal Learning Threshold (BOLT). This loss trains classifiers by minimizing an expected term that upper-bounds the true Bayes error rate. Experiments show BOLT training often achieves lower held-out classification errors than standard cross-entropy training.

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