Convergence Analysis of Distributed Stochastic Gradient Descent with Shuffling

29 Sep 2017Qi MengWei ChenYue WangZhi-Ming MaTie-Yan Liu

When using stochastic gradient descent to solve large-scale machine learning problems, a common practice of data processing is to shuffle the training data, partition the data across multiple machines if needed, and then perform several epochs of training on the re-shuffled (either locally or globally) data. The above procedure makes the instances used to compute the gradients no longer independently sampled from the training data set... (read more)

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