Bilevel optimization is applied to neural network training to produce self-calibrated confidence scores, reducing calibration error versus isotonic regression on toy and simulated BAC datasets while preserving accuracy.
Domke, Generic methods for optimization-based modeling, in: Artificial Intelligence and Statistics, PMLR, 2012, pp
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Exploring the Potential of Bilevel Optimization for Calibrating Neural Networks
Bilevel optimization is applied to neural network training to produce self-calibrated confidence scores, reducing calibration error versus isotonic regression on toy and simulated BAC datasets while preserving accuracy.