mlsanityData to 7 Oct 2026

Bootstrap SGD: Algorithmic Stability and Robustness

arXiv 2409.01074 ↗First posted 2 Sep 2024stat.ML cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 33 references that are in the map. First posted on arXiv 2 Sep 2024.

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14 of 33 references.

References · 14

  1. 2015Train faster, generalize better: Stability of stochastic gradient descentcited by 547
  2. 2019Online Learning: A Modern Introduction Using Convex Optimizationcited by 407
  3. 2020Fine-Grained Analysis of Stability and Generalization for Stochastic Gradient Descentcited by 118
  4. 2021Information-Theoretic Generalization Bounds for Stochastic Gradient Descentcited by 84
  5. 2017Algorithmic stability and hypothesis complexitycited by 39
  6. 2022Stability vs Implicit Bias of Gradient Methods on Separable Data and Beyondcited by 29
  7. 2021Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernelcited by 25
  8. 2022Stability and Generalization Analysis of Gradient Methods for Shallow Neural Networkscited by 21
  9. 2023Generalization and Stability of Interpolating Neural Networks with Minimal Widthcited by 21
  10. 2022Beyond Lipschitz: Sharp Generalization and Excess Risk Bounds for Full-Batch GDcited by 19
  11. 2022Generalization Bounds of Nonconvex-(Strongly)-Concave Stochastic Minimax Optimizationcited by 6
  12. 2023Minibatch and Local SGD: Algorithmic Stability and Linear Speedup in Generalizationcited by 5
2 more
  1. 2015On the Robustness of Regularized Pairwise Learning Methods Based on Kernelscited by 4
  2. 2013On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel Based Methodscited by 2

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