Generalization of Hamiltonian algorithms

Andreas Maurer

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arXiv · 2405.14469

Generalization of Hamiltonian algorithms

Andreas Maurer

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Abstract

The paper proves generalization results for a class of stochastic learning algorithms. The method applies whenever the algorithm generates an absolutely continuous distribution relative to some a-priori measure and the Radon Nikodym derivative has subgaussian concentration. Applications are bounds for the Gibbs algorithm and randomizations of stable deterministic algorithms as well as PAC-Bayesian bounds with data-dependent priors.

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Andreas Maurer. 2024-05-23. Generalization of Hamiltonian algorithms. https://arxiv.org/abs/2405.14469

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