Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Modular Gaussian Processes for Transfer Learning   We present a framework for transfer learning based on modular variational
Gaussian processes (GP). We develop a module-based method that having a
dictionary of well fitted GPs, one could build ensemble GP models without
revisiting any data. Each model is characterised by its hyperparameters,
pseudo-inputs and their corresponding posterior densities. Our method avoids
undesired data centralisation, reduces rising computational costs and allows
the transfer of learned uncertainty metrics after training. We exploit the
augmentation of high-dimensional integral operators based on the
Kullback-Leibler divergence between stochastic processes to introduce an
efficient lower bound under all the sparse variational GPs, with different
complexity and even likelihood distribution. The method is also valid for
multi-output GPs, learning correlations a posteriori between independent
modules. Extensive results illustrate the usability of our framework in
large-scale and multi-task experiments, also compared with the exact inference
methods in the literature.

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The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph
7
The categories of this paper are: hep-th