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We introduce a new PAC-Bayes oracle bound for unbounded losses that extends Cram\'er-Chernoff bounds to the PAC-Bayesian setting.
Asymptotic evaluation of certain Markov process expectations for large time, I
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Jiantao Jiao, Yanjun Han, and Tsachy Weissman · 2017
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Andrés Masegosa · 2020
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Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvári, and John Shawe-Taylor · 2020
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Pradeep Kr Banerjee and Guido Montúfar · 2021
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Scaleable input gradient regularization for adversarial robustness
Chris Finlay and Adam M Oberman · 2021
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Andrew Foong, Wessel Bruinsma, David Burt, and Richard Turner · 2021
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Learning Gaussian processes by minimizing PAC-Bayesian generalization bounds
David Reeb, Andreas Doerr, Sebastian Gerwinn, and Barbara Rakitsch · 2018
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Still no free lunches: the price to pay for tighter PAC-Bayes bounds
Benjamin Guedj and Louis Pujol · 2021
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PAC-Bayes unleashed: Generalisation bounds with unbounded losses
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Fredrik Hellström and Giuseppe Durisi · 2021
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Diversity and generalization in neural network ensembles
Luis A Ortega, Rafael Cabañas, and Andres Masegosa · 2022
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A unified recipe for deriving (time-uniform) PAC-Bayes bounds
Ben Chugg, Hongjian Wang, and Aaditya Ramdas · 2023
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Generalization Bounds: Perspectives from Information Theory and PAC-Bayes
Fredrik Hellström, Giuseppe Durisi, Benjamin Guedj, and Maxim Raginsky · 2023
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PAC-Chernoff Bounds: Understanding Generalization in the Interpolation Regime
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