Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fast Black-box Variational Inference through Stochastic Trust-Region
  Optimization   We introduce TrustVI, a fast second-order algorithm for black-box variational
inference based on trust-region optimization and the reparameterization trick.
At each iteration, TrustVI proposes and assesses a step based on minibatches of
draws from the variational distribution. The algorithm provably converges to a
stationary point. We implemented TrustVI in the Stan framework and compared it
to two alternatives: Automatic Differentiation Variational Inference (ADVI) and
Hessian-free Stochastic Gradient Variational Inference (HFSGVI). The former is
based on stochastic first-order optimization. The latter uses second-order
information, but lacks convergence guarantees. TrustVI typically converged at
least one order of magnitude faster than ADVI, demonstrating the value of
stochastic second-order information. TrustVI often found substantially better
variational distributions than HFSGVI, demonstrating that our convergence
theory can matter in practice.

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The categories of this paper are: funct-an
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The categories of this paper are: atom-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
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The categories of this paper are: hep-th
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The categories of this paper are: q-fin
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The categories of this paper are: nucl-th
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The categories of this paper are: nucl-ex
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The categories of this paper are: nlin
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The categories of this paper are: gr-qc
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The categories of this paper are: comp-gas
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The categories of this paper are: cmp-lg
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The categories of this paper are: ao-sci
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The categories of this paper are: mtrl-th