Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptive Expansion Bayesian Optimization for Unbounded Global
  Optimization   Bayesian optimization is normally performed within fixed variable bounds. In
cases like hyperparameter tuning for machine learning algorithms, setting the
variable bounds is not trivial. It is hard to guarantee that any fixed bounds
will include the true global optimum. We propose a Bayesian optimization
approach that only needs to specify an initial search space that does not
necessarily include the global optimum, and expands the search space when
necessary. However, over-exploration may occur during the search space
expansion. Our method can adaptively balance exploration and exploitation in an
expanding space. Results on a range of synthetic test functions and an MLP
hyperparameter optimization task show that the proposed method out-performs or
at least as good as the current state-of-the-art methods.

37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg
28
The categories of this paper are: alg-geom
0
The categories of this paper are: hep-ph
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an