Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: KL-based Control of the Learning Schedule for Surrogate Black-Box
  Optimization   This paper investigates the control of an ML component within the Covariance
Matrix Adaptation Evolution Strategy (CMA-ES) devoted to black-box
optimization. The known CMA-ES weakness is its sample complexity, the number of
evaluations of the objective function needed to approximate the global optimum.
This weakness is commonly addressed through surrogate optimization, learning an
estimate of the objective function a.k.a. surrogate model, and replacing most
evaluations of the true objective function with the (inexpensive) evaluation of
the surrogate model. This paper presents a principled control of the learning
schedule (when to relearn the surrogate model), based on the Kullback-Leibler
divergence of the current search distribution and the training distribution of
the former surrogate model. The experimental validation of the proposed
approach shows significant performance gains on a comprehensive set of
ill-conditioned benchmark problems, compared to the best state of the art
including the quasi-Newton high-precision BFGS method.

26
The categories of this paper are: bayes-an
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
10
The categories of this paper are: q-bio
34
The categories of this paper are: chem-ph
6
The categories of this paper are: astro-ph
25
The categories of this paper are: patt-sol
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess