Generalization for slowly mixing processes
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 10 of its 40 references that are in the map. First posted on arXiv 28 Apr 2023.
Builds on
10 of 40 references.
References · 10
- 2017Spectrally-normalized margin bounds for neural networkscited by 681
- 2014Convex Optimization: Algorithms and Complexitycited by 569
- 2020Exactly Computing the Local Lipschitz Constant of ReLU Networkscited by 102
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- 2013Predictive PAC Learning and Process Decompositionscited by 9
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Citing papers per year: 2024: 1.
1 paper.