Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Batched Stochastic Gradient Descent with Weighted Sampling   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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The categories of this paper are: plasm-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: dg-ga
3
The categories of this paper are: physics
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The categories of this paper are: acc-phys
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-alg
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The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-ex
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The categories of this paper are: math-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: q-fin
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The categories of this paper are: nlin
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The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc