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Researchers develop novel bootstrap for SGD confidence sets

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel method for constructing confidence sets in Stochastic Gradient Descent (SGD) algorithms. This new approach utilizes the multiplier bootstrap procedure and establishes its non-asymptotic validity. The method achieves approximation rates of order $1/\sqrt{n}$, which can be faster than existing central limit theorem approaches, and provides the first fully non-asymptotic bound on bootstrap accuracy in SGD. AI

IMPACT Introduces a novel statistical technique that could improve the reliability of machine learning model training.

RANK_REASON This is a research paper detailing a new statistical method for machine learning algorithms. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Researchers develop novel bootstrap for SGD confidence sets

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This is a research paper detailing a new statistical method for machine learning algorithms. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Marina Sheshukova, Sergey Samsonov, Denis Belomestny, Eric Moulines, Qi-Man Shao, Zhuo-Song Zhang, Alexey Naumov ·

    Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient Descent

    arXiv:2502.06719v3 Announce Type: replace Abstract: In this paper, we establish the non-asymptotic validity of the multiplier bootstrap procedure for constructing the confidence sets using the Stochastic Gradient Descent (SGD) algorithm. Under appropriate regularity conditions, o…

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