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• W2963244042 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." @default.
• W2963244042 created "2019-07-30" @default.
• W2963244042 creator A5024153474 @default.
• W2963244042 creator A5040543665 @default.
• W2963244042 date "2017-01-01" @default.
• W2963244042 modified "2023-10-16" @default.
• W2963244042 title "Batched Stochastic Gradient Descent with Weighted Sampling" @default.
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• W2963244042 doi "https://doi.org/10.1007/978-3-319-59912-0_14" @default.
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