Generalization of Hamiltonian algorithms

• URL: http://arxiv.org/abs/2405.14469v2
• Date: Thu, 29 Aug 2024 15:41:52 GMT
• Title: Generalization of Hamiltonian algorithms
• Authors: Andreas Maurer,
• Abstract summary: The paper proves generalization results for a class of learning algorithms. The method applies whenever the algorithm generates an absolutely continuous distribution relative to some a-priori measure. Applications are bounds for the Gibbs algorithm and randomizations of stable deterministic algorithms as well as PAC-Bayesian bounds with data-dependent priors.
• Score: 6.835035668445878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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