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A Chain Rule for the Expected Suprema of Bernoulli Processes

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arxiv 2304.14474 v1 pith:5BLGC34X submitted 2023-04-27 math.PR cs.LGstat.ML

classification math.PRcs.LGstat.ML
keywords bernoulliprocessesclassexpectedfunctionindexbednorzbound
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We obtain an upper bound on the expected supremum of a Bernoulli process indexed by the image of an index set under a uniformly Lipschitz function class in terms of properties of the index set and the function class, extending an earlier result of Maurer for Gaussian processes. The proof makes essential use of recent results of Bednorz and Latala on the boundedness of Bernoulli processes.

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Cited by 1 Pith paper

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  1. On Generalization Bounds for Neural Networks with Low Rank Layers

    cs.LG 2024-11 reject novelty 5.0 of 10

    Low-rank layers in deep networks yield Gaussian complexity bounds where the rank factor appears once, not once per layer, improving on prior norm-based bounds.

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