Shen-Yi Zhao

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Alternating direction method of multipliers (ADMM) has been widely used in many applications due to its promising performance to solve complex regularization problems and large-scale distributed optimization problems. Stochastic ADMM, which visits only one sample or a mini-batch of samples each time, has recently been proved to achieve better performance(More)
Stochastic gradient descent (SGD) and its variants have become more and more popular in machine learning due to their efficiency and effectiveness. To handle large-scale problems, researchers have recently proposed several parallel SGD methods for multicore systems. However, existing parallel SGD methods cannot achieve satisfactory performance in real(More)
Many machine learning models, such as logistic regression (LR) and support vector machine (SVM), can be formulated as composite optimization problems. Recently, many distributed stochastic optimization (DSO) methods have been proposed to solve the large-scale composite optimization problems, which have shown better performance than traditional batch(More)
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