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A general framework for the practical disintegration of PAC-Bayesian bounds.

, , , and . Mach. Learn., 113 (2): 519-604 (February 2024)

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A PAC-Bayes Sample-compression Approach to Kernel Methods., , , , and . ICML, page 297-304. Omnipress, (2011)PAC-Bayesian Learning of Aggregated Binary Activated Neural Networks with Probabilities over Representations., , , , and . Canadian AI, Canadian Artificial Intelligence Association, (2023)A PAC-Bayes Risk Bound for General Loss Functions., , , and . NIPS, page 449-456. MIT Press, (2006)Landmark-Based Ensemble Learning with Random Fourier Features and Gradient Boosting., , , , , , and . ECML/PKDD (3), volume 12459 of Lecture Notes in Computer Science, page 141-157. Springer, (2020)A Greedy Algorithm for Building Compact Binary Activated Neural Networks., and . CoRR, (2022)Interpretable Domain Adaptation for Hidden Subdomain Alignment in the Context of Pre-trained Source Models., , , and . AAAI, page 9057-9065. AAAI Press, (2022)From PAC-Bayes Bounds to KL Regularization., , , , and . NIPS, page 603-610. Curran Associates, Inc., (2009)Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound., , , , , , and . NeurIPS, page 455-467. (2021)Phoneme Discretized Saliency Maps for Explainable Detection of AI-Generated Voice., , , and . CoRR, (2024)Domain-Adversarial Training of Neural Networks., , , , , , , and . J. Mach. Learn. Res., (2016)