Generalization for slowly mixing processes
Machine Learning
2023-06-02 v2
Abstract
A bound uniform over various loss-classes is given for data generated by stationary and phi-mixing processes, where the mixing time (the time needed to obtain approximate independence) enters the sample complexity only in an additive way. For slowly mixing processes this can be a considerable advantage over results with multiplicative dependence on the mixing time. The admissible loss-classes include functions with prescribed Lipschitz norms or smoothness parameters. The bound can also be applied to be uniform over unconstrained loss-classes, where it depends on local Lipschitz properties of the function on the sample path.
Cite
@article{arxiv.2305.00977,
title = {Generalization for slowly mixing processes},
author = {Andreas Maurer},
journal= {arXiv preprint arXiv:2305.00977},
year = {2023}
}
Comments
Improved version