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Progress in Self-Certified Neural Networks
We then show that in data starvation regimes, holding out data for the test set bounds adversely affects generalisation performance, while self-certified strategies based on PAC-Bayes bounds do not suffer from this drawback, proving that they might be a suitable choice for the small data regime.
PAC-Bayes bounds for stable algorithms with instance-dependent priors
Our main result estimates the risk of the randomized algorithm in terms of the hypothesis stability coefficients.
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