Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: L-SVRG and L-Katyusha with Adaptive Sampling   Stochastic gradient-based optimization methods, such as L-SVRG and its
accelerated variant L-Katyusha [12], are widely used to train machine learning
models. Theoretical and empirical performance of L-SVRG and L-Katyusha can be
improved by sampling the observations from a non-uniform distribution [17].
However, to design a desired sampling distribution, Qian et al.[17] rely on
prior knowledge of smoothness constants that can be computationally intractable
to obtain in practice when the dimension of the model parameter is high. We
propose an adaptive sampling strategy for L-SVRG and L-Katyusha that learns the
sampling distribution with little computational overhead, while allowing it to
change with iterates, and at the same time does not require any prior knowledge
on the problem parameters. We prove convergence guarantees for L-SVRG and
L-Katyusha for convex objectives when the sampling distribution changes with
iterates. These results show that even without prior information, the proposed
adaptive sampling strategy matches, and in some cases even surpasses, the
performance of the sampling scheme in Qian et al.[17]. Extensive simulations
support our theory and the practical utility of the proposed sampling scheme on
real data.

27
The categories of this paper are: comp-gas
20
The categories of this paper are: acc-phys
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
10
The categories of this paper are: q-bio
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
15
The categories of this paper are: nucl-ex
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The categories of this paper are: eess
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The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg