@techreport{picard2024surrogate,
title = {Optimising {Maurer}-{Langford}-{Seeger}'s bound},
author = {Picard-Weibel, Antoine and Guedj, Benjamin},
year = {2024},
note = "Short note.",
type = {Research Report},
abstract = {The surrogate PAC-Bayes learning algorithm was recently introduced by Picard-Weibel et al. (2024) to efficiently minimise the bound of Catoni. Catoni's celebrated bound is tractable, however the Maurer-Langford-Seeger (MLS) bound is generally tighter. In this short note, we adapt the surrogate PAC-Bayes  framework to efficiently minimise the MLS bound.},
url = {https://arxiv.org/abs/2411.xxxxx},
url_PDF = {https://arxiv.org/pdf/2411.xxxxx.pdf},
doi = {10.48550/ARXIV.2411.xxxxx},
eprint={2411.xxxxx},
archivePrefix={arXiv},
primaryClass={stat.ML},
copyright = {Creative Commons Attribution 4.0 International},
keywords={mine}
}

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