Benign Underfitting of Stochastic Gradient Descent

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


We study to what extent may stochastic gradient descent (SGD) be understood as a “conventional” learning rule that achieves generalization performance by obtaining a good fit to training data. We consider the fundamental stochastic convex optimization framework, where (one pass, without-replacement) SGD is classically known to minimize the population risk at rate O(1/√n), and prove that, surprisingly, there exist problem instances where the SGD solution exhibits both empirical risk and generalization gap of Ω(1). Consequently, it turns out that SGD is not algorithmically stable in any sense, and its generalization ability cannot be explained by uniform convergence or any other currently known generalization bound technique for that matter (other than that of its classical analysis). We then continue to analyze the closely related with-replacement SGD, for which we show that an analogous phenomenon does not occur and prove that its population risk does in fact converge at the optimal rate. Finally, we interpret our main results in the context of without-replacement SGD for finite-sum convex optimization problems, and derive upper and lower bounds for the multi-epoch regime that significantly improve upon previously known results.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 35 - 36th Conference on Neural Information Processing Systems, NeurIPS 2022
EditorsS. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, A. Oh
PublisherNeural information processing systems foundation
ISBN (Electronic)9781713871088
StatePublished - 2022
Event36th Conference on Neural Information Processing Systems, NeurIPS 2022 - New Orleans, United States
Duration: 28 Nov 20229 Dec 2022

Publication series

NameAdvances in Neural Information Processing Systems
ISSN (Print)1049-5258


Conference36th Conference on Neural Information Processing Systems, NeurIPS 2022
Country/TerritoryUnited States
CityNew Orleans


FundersFunder number
Yandex Initiative in Machine Learning
Horizon 2020 Framework Programme
Blavatnik Family Foundation
European Research Council
Israel Science Foundation2549/19, 2188/20, 993/17
Tel Aviv University
Horizon 2020882396


    Dive into the research topics of 'Benign Underfitting of Stochastic Gradient Descent'. Together they form a unique fingerprint.

    Cite this