Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: High Probability Complexity Bounds for Adaptive Line Search Based on
  Stochastic Oracles   We consider a line-search method for continuous optimization under a
stochastic setting where the function values and gradients are available only
through inexact probabilistic zeroth and first-order oracles. These oracles
capture multiple standard settings including expected loss minimization and
zeroth-order optimization. Moreover, our framework is very general and allows
the function and gradient estimates to be biased. The proposed algorithm is
simple to describe, easy to implement, and uses these oracles in a similar way
as the standard deterministic line search uses exact function and gradient
values. Under fairly general conditions on the oracles, we derive a high
probability tail bound on the iteration complexity of the algorithm for
non-convex, convex, and strongly convex functions. These results are stronger
than those for other existing stochastic line search methods and apply in more
general settings.

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The categories of this paper are: bayes-an
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The categories of this paper are: adap-org
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The categories of this paper are: chao-dyn
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The categories of this paper are: funct-an
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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: patt-sol
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The categories of this paper are: nucl-th
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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-bio
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The categories of this paper are: nlin
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The categories of this paper are: hep-lat
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The categories of this paper are: astro-ph