Generalization of Hamiltonian algorithms
<|reference_start|>Generalization of Hamiltonian algorithms: 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.<|reference_end|>
@article{maurer2024generalization,
    title={Generalization of Hamiltonian algorithms},
    author={Andreas Maurer},
    journal={arXiv preprint arXiv:2405.14469},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.14469},
    primaryClass={cs.LG}
}