Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Calibration Improves Bayesian Optimization   Bayesian optimization is a procedure that allows obtaining the global optimum
of black-box functions and that is useful in applications such as
hyper-parameter optimization. Uncertainty estimates over the shape of the
objective function are instrumental in guiding the optimization process.
However, these estimates can be inaccurate if the objective function violates
assumptions made within the underlying model (e.g., Gaussianity). We propose a
simple algorithm to calibrate the uncertainty of posterior distributions over
the objective function as part of the Bayesian optimization process. We show
that by improving the uncertainty estimates of the posterior distribution with
calibration, Bayesian optimization makes better decisions and arrives at the
global optimum in fewer steps. We show that this technique improves the
performance of Bayesian optimization on standard benchmark functions and
hyperparameter optimization tasks.

12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
11
The categories of this paper are: hep-lat
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
18
The categories of this paper are: econ