Statistical Optimality of Stochastic Gradient Descent on Hard Learning Problems through Multiple Passes

We consider stochastic gradient descent (SGD) for least-squares regression with potentially several passes over the data. While several passes have been widely reported to perform practically better in terms of predictive performance on unseen data, the existing theoretical analysis of SGD suggests that a single pass is statistically optimal... (read more)

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