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arXiv · 2002.10597

Statistical Adaptive Stochastic Gradient Methods

Pengchuan Zhang·Hunter Lang·Qiang Liu·Lin Xiao
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Abstract

We propose a statistical adaptive procedure called SALSA for automatically scheduling the learning rate (step size) in stochastic gradient methods. SALSA first uses a smoothed stochastic line-search procedure to gradually increase the learning rate, then automatically switches to a statistical method to decrease the learning rate. The line search procedure ``warms up'' the optimization process, reducing the need for expensive trial and error in setting an initial learning rate. The method for decreasing the learning rate is based on a new statistical test for detecting stationarity when using a constant step size. Unlike in prior work, our test applies to a broad class of stochastic gradient algorithms without modification. The combined method is highly robust and autonomous, and it matches the performance of the best hand-tuned learning rate schedules in our experiments on several deep learning tasks.

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Pengchuan Zhang, Hunter Lang, Qiang Liu, Lin Xiao. 2020-02-25. Statistical Adaptive Stochastic Gradient Methods. https://arxiv.org/abs/2002.10597

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