Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Parallel Gaussian Process Optimization with Upper Confidence Bound and
  Pure Exploration   In this paper, we consider the challenge of maximizing an unknown function f
for which evaluations are noisy and are acquired with high cost. An iterative
procedure uses the previous measures to actively select the next estimation of
f which is predicted to be the most useful. We focus on the case where the
function can be evaluated in parallel with batches of fixed size and analyze
the benefit compared to the purely sequential procedure in terms of cumulative
regret. We introduce the Gaussian Process Upper Confidence Bound and Pure
Exploration algorithm (GP-UCB-PE) which combines the UCB strategy and Pure
Exploration in the same batch of evaluations along the parallel iterations. We
prove theoretical upper bounds on the regret with batches of size K for this
procedure which show the improvement of the order of sqrt{K} for fixed
iteration cost over purely sequential versions. Moreover, the multiplicative
constants involved have the property of being dimension-free. We also confirm
empirically the efficiency of GP-UCB-PE on real and synthetic problems compared
to state-of-the-art competitors.

23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
34
The categories of this paper are: chem-ph
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
3
The categories of this paper are: physics
32
The categories of this paper are: atom-ph
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci