Bootstrap SGD: Algorithmic Stability and Robustness

Andreas Christmann; Yunwen Lei

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arXiv · 2409.01074

Bootstrap SGD: Algorithmic Stability and Robustness

Andreas Christmann·Yunwen Lei

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Abstract

In this paper some methods to use the empirical bootstrap approach for stochastic gradient descent (SGD) to minimize the empirical risk over a separable Hilbert space are investigated from the view point of algorithmic stability and statistical robustness. The first two types of approaches are based on averages and are investigated from a theoretical point of view. A generalization analysis for bootstrap SGD of Type 1 and Type 2 based on algorithmic stability is done. Another type of bootstrap SGD is proposed to demonstrate that it is possible to construct purely distribution-free pointwise confidence intervals of the median curve using bootstrap SGD.

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BibTeX RIS

Andreas Christmann, Yunwen Lei. 2024-09-02. Bootstrap SGD: Algorithmic Stability and Robustness. https://arxiv.org/abs/2409.01074

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