Optimal Transport PAC-Bayes Bounds

Abstract:PAC-Bayesian generalization bounds are among the strongest distribution-dependent guarantees in statistical learning and are typically expressed in terms of the Kullback-Leibler (KL) divergence between a prior and posterior distribution. While analytically convenient, the KL divergence requires the posterior to be absolutely continuous w.r.t. the prior, imposing support constraints that can substantially restrict the choice of posterior. Moreover, the KL divergence does not account for the underlying geometry of the probability space. In this work, we derive new PAC-Bayesian bounds replacing the KL divergence with general optimal transport (OT) distances, including the total variation and Wasserstein-$p$ distances.

Machine Learning (cs.LG)

arXiv:2610.16560 [cs.LG]

(or arXiv:2610.16560v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.16560 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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