Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Almost Tune-Free Variance Reduction   The variance reduction class of algorithms including the representative ones,
SVRG and SARAH, have well documented merits for empirical risk minimization
problems. However, they require grid search to tune parameters (step size and
the number of iterations per inner loop) for optimal performance. This work
introduces `almost tune-free' SVRG and SARAH schemes equipped with i)
Barzilai-Borwein (BB) step sizes; ii) averaging; and, iii) the inner loop
length adjusted to the BB step sizes. In particular, SVRG, SARAH, and their BB
variants are first reexamined through an `estimate sequence' lens to enable new
averaging methods that tighten their convergence rates theoretically, and
improve their performance empirically when the step size or the inner loop
length is chosen large. Then a simple yet effective means to adjust the number
of iterations per inner loop is developed to enhance the merits of the proposed
averaging schemes and BB step sizes. Numerical tests corroborate the proposed
methods.

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The categories of this paper are: q-bio
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The categories of this paper are: quant-ph
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The categories of this paper are: econ
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The categories of this paper are: patt-sol
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The categories of this paper are: cmp-lg
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The categories of this paper are: comp-gas
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The categories of this paper are: acc-phys
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The categories of this paper are: atom-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: cond-mat
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The categories of this paper are: alg-geom
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The categories of this paper are: hep-ex
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The categories of this paper are: funct-an
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The categories of this paper are: gr-qc
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The categories of this paper are: bayes-an
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The categories of this paper are: math-ph