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.
Platt, et al., Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods, Advances in large margin classifiers 10 (1999) 61–74
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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.