Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: BayesOpt: A Library for Bayesian optimization with Robotics Applications   The purpose of this paper is twofold. On one side, we present a general
framework for Bayesian optimization and we compare it with some related fields
in active learning and Bayesian numerical analysis. On the other hand, Bayesian
optimization and related problems (bandits, sequential experimental design) are
highly dependent on the surrogate model that is selected. However, there is no
clear standard in the literature. Thus, we present a fast and flexible toolbox
that allows to test and combine different models and criteria with little
effort. It includes most of the state-of-the-art contributions, algorithms and
models. Its speed also removes part of the stigma that Bayesian optimization
methods are only good for "expensive functions". The software is free and it
can be used in many operating systems and computer languages.

13
The categories of this paper are: quant-ph
23
The categories of this paper are: solv-int
16
The categories of this paper are: stat
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
25
The categories of this paper are: patt-sol
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
31
The categories of this paper are: ao-sci
4
The categories of this paper are: cond-mat
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc