We study PAC-Bayes derandomization for smooth loss functions. Our goal is to obtain generalization bounds that hold with high probability for deterministic predictors by exploiting smoothness properties of both the loss and the predictor class.
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arXiv:2606. 19105v1 Announce Type: new Abstract: We study PAC-Bayes derandomization for smooth loss functions.
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