Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Solving Stochastic Compositional Optimization is Nearly as Easy as
  Solving Stochastic Optimization   Stochastic compositional optimization generalizes classic (non-compositional)
stochastic optimization to the minimization of compositions of functions. Each
composition may introduce an additional expectation. The series of expectations
may be nested. Stochastic compositional optimization is gaining popularity in
applications such as reinforcement learning and meta learning. This paper
presents a new Stochastically Corrected Stochastic Compositional gradient
method (SCSC). SCSC runs in a single-time scale with a single loop, uses a
fixed batch size, and guarantees to converge at the same rate as the stochastic
gradient descent (SGD) method for non-compositional stochastic optimization.
This is achieved by making a careful improvement to a popular stochastic
compositional gradient method. It is easy to apply SGD-improvement techniques
to accelerate SCSC. This helps SCSC achieve state-of-the-art performance for
stochastic compositional optimization. In particular, we apply Adam to SCSC,
and the exhibited rate of convergence matches that of the original Adam on
non-compositional stochastic optimization. We test SCSC using the portfolio
management and model-agnostic meta-learning tasks.

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The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
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The categories of this paper are: dg-ga
16
The categories of this paper are: stat
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The categories of this paper are: econ
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
12
The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
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The categories of this paper are: nlin
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The categories of this paper are: eess
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The categories of this paper are: bayes-an
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The categories of this paper are: q-bio