Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: One-parameter family of acquisition functions for efficient global
  optimization   Bayesian optimization (BO) with Gaussian processes is a powerful methodology
to optimize an expensive black-box function with as few function evaluations as
possible. The expected improvement (EI) and probability of improvement (PI) are
among the most widely used schemes for BO. There is a plethora of other schemes
that outperform EI and PI, but most of them are numerically far more expensive
than EI and PI. In this work, we propose a new one-parameter family of
acquisition functions for BO that unifies EI and PI. The proposed method is
numerically inexpensive, is easy to implement, can be easily parallelized, and
on benchmark tasks shows a performance superior to EI and GP-UCB. Its
generalization to BO with Student-t processes is also presented.

20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
28
The categories of this paper are: alg-geom
19
The categories of this paper are: eess
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
34
The categories of this paper are: chem-ph
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat