354 citations · 2.1k across the 86 of their papers we have counts for
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stat.ML2014★ 84 cited
Accelerating Minibatch Stochastic Gradient Descent using Stratified Sampling
Peilin Zhao, Tong Zhang
Stochastic Gradient Descent (SGD) is a popular optimization method which has been applied to many important machine learning tasks such as Support Vector Machines and Deep Neural N…
stat.ML2014★ 62 cited
Stochastic Optimization with Importance Sampling
Peilin Zhao, Tong Zhang
Uniform sampling of training data has been commonly used in traditional stochastic optimization algorithms such as Proximal Stochastic Gradient Descent (prox-SGD) and Proximal Stoc…