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Progress in Self-Certified Neural Networks
[article]
2021
arXiv
pre-print
A learning method is self-certified if it uses all available data to simultaneously learn a predictor and certify its quality with a tight statistical certificate that is valid on unseen data. Recent work has shown that neural network models trained by optimising PAC-Bayes bounds lead not only to accurate predictors, but also to tight risk certificates, bearing promise towards achieving self-certified learning. In this context, learning and certification strategies based on PAC-Bayes bounds are
arXiv:2111.07737v3
fatcat:2ixoizo5pnemjc3jhzo4rthh3y