Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Nonasymptotic estimates for Stochastic Gradient Langevin Dynamics under
  local conditions in nonconvex optimization   Within the context of empirical risk minimization, see Raginsky, Rakhlin, and
Telgarsky (2017), we are concerned with a non-asymptotic analysis of sampling
algorithms used in optimization. In particular, we obtain non-asymptotic error
bounds for a popular class of algorithms called Stochastic Gradient Langevin
Dynamics (SGLD). These results are derived in Wasserstein-1 and Wasserstein-2
distances in the absence of log-concavity of the target distribution. More
precisely, the stochastic gradient $H(\theta, x)$ is assumed to be locally
Lipschitz continuous in both variables, and furthermore, the dissipativity
condition is relaxed by removing its uniform dependence in $x$. This relaxation
allows us to present two key paradigms within the framework of scalable
posterior sampling for Bayesian inference and of nonconvex optimization;
namely, examples from minibatch logistic regression and from variational
inference are given by providing theoretical guarantees for the sampling
behaviour of the algorithm.

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The categories of this paper are: atom-ph
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The categories of this paper are: funct-an
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The categories of this paper are: chao-dyn
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: ao-sci
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The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
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The categories of this paper are: hep-lat
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-ex
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The categories of this paper are: eess
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The categories of this paper are: physics
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The categories of this paper are: chem-ph