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Alibek Sailanbayev

2 accepted papers

2023

Random Reshuffling with Variance Reduction: New Analysis and Better Rates

UAI 2023poster

Virtually all state-of-the-art methods for training supervised machine learning models are variants of Stochastic Gradient Descent (SGD), enhanced with a number of additional tricks, such as minibatching, momentum, and adaptive stepsizes. However, one of the most basic questions in the design of su…

Cited by 24SourcePDFScholar
2019

SGD: General Analysis and Improved Rates

ICML 2019oral

We propose a general yet simple theorem describing the convergence of SGD under the arbitrary sampling paradigm. Our theorem describes the convergence of an infinite array of variants of SGD, each of which is associated with a specific probability law governing the data selection rule used to form m…

Cited by 557SourcePDFScholar