Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multiple Adaptive Bayesian Linear Regression for Scalable Bayesian
  Optimization with Warm Start   Bayesian optimization (BO) is a model-based approach for gradient-free
black-box function optimization. Typically, BO is powered by a Gaussian process
(GP), whose algorithmic complexity is cubic in the number of evaluations.
Hence, GP-based BO cannot leverage large amounts of past or related function
evaluations, for example, to warm start the BO procedure. We develop a multiple
adaptive Bayesian linear regression model as a scalable alternative whose
complexity is linear in the number of observations. The multiple Bayesian
linear regression models are coupled through a shared feedforward neural
network, which learns a joint representation and transfers knowledge across
machine learning problems.

20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph