Batched Stochastic Gradient Descent with Weighted Sampling

Deanna Needell; Rachel Ward

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arXiv · 1608.07641

Batched Stochastic Gradient Descent with Weighted Sampling

Deanna Needell·Rachel Ward

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Abstract

We analyze a batched variant of Stochastic Gradient Descent (SGD) with weighted sampling distribution for smooth and non-smooth objective functions. We show that by distributing the batches computationally, a significant speedup in the convergence rate is provably possible compared to either batched sampling or weighted sampling alone. We propose several computationally efficient schemes to approximate the optimal weights, and compute proposed sampling distributions explicitly for the least squares and hinge loss problems. We show both analytically and experimentally that substantial gains can be obtained.

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Deanna Needell, Rachel Ward. 2016-08-27. Batched Stochastic Gradient Descent with Weighted Sampling. https://arxiv.org/abs/1608.07641

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