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PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction
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We propose an algorithm combining calibrated prediction and generalization bounds from learning theory to construct confidence sets for deep neural networks with PAC guarantees---i.e., the confidence set for a given input contains the true label with high probability. We demonstrate how our approach can be used to construct PAC confidence sets on ResNet for ImageNet, a visual object tracking model, and a dynamics model for the half-cheetah reinforcement learning problem.
Forward citations
Cited by 2 Pith papers
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Position: There Is No Free Bayesian Uncertainty Quantification
Bayesian updating is reframed as an optimization problem without inherent uncertainty quantification, and a PAC-style calibration step is proposed to give predictive intervals frequentist coverage.
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