39 citations · 154 across the 26 of their papers we have counts for
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math.OC2020★ 29 cited
The Impact of the Mini-batch Size on the Variance of Gradients in Stochastic Gradient Descent
Xin Qian, Diego Klabjan
The mini-batch stochastic gradient descent (SGD) algorithm is widely used in training machine learning models, in particular deep learning models. We study SGD dynamics under linea…
math.OC2019
Scale Invariant Power Iteration
Cheolmin Kim, Youngseok Kim, Diego Klabjan
Power iteration has been generalized to solve many interesting problems in machine learning and statistics. Despite its striking success, theoretical understanding of when and how…
math.OC2019★ 2 cited
Stochastic Variance-Reduced Heavy Ball Power Iteration
Cheolmin Kim, Diego Klabjan
We present a stochastic variance-reduced heavy ball power iteration algorithm for solving PCA and provide a convergence analysis for it. The algorithm is an extension of heavy ball…