Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning search spaces for Bayesian optimization: Another view of
  hyperparameter transfer learning   Bayesian optimization (BO) is a successful methodology to optimize black-box
functions that are expensive to evaluate. While traditional methods optimize
each black-box function in isolation, there has been recent interest in
speeding up BO by transferring knowledge across multiple related black-box
functions. In this work, we introduce a method to automatically design the BO
search space by relying on evaluations of previous black-box functions. We
depart from the common practice of defining a set of arbitrary search ranges a
priori by considering search space geometries that are learned from historical
data. This simple, yet effective strategy can be used to endow many existing BO
methods with transfer learning properties. Despite its simplicity, we show that
our approach considerably boosts BO by reducing the size of the search space,
thus accelerating the optimization of a variety of black-box optimization
problems. In particular, the proposed approach combined with random search
results in a parameter-free, easy-to-implement, robust hyperparameter
optimization strategy. We hope it will constitute a natural baseline for
further research attempting to warm-start BO.

22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
36
The categories of this paper are: cmp-lg
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga