Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-task learning on the edge: cost-efficiency and theoretical
  optimality   This article proposes a distributed multi-task learning (MTL) algorithm based
on supervised principal component analysis (SPCA) which is: (i) theoretically
optimal for Gaussian mixtures, (ii) computationally cheap and scalable.
Supporting experiments on synthetic and real benchmark data demonstrate that
significant energy gains can be obtained with no performance loss.

23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an
31
The categories of this paper are: ao-sci
34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
19
The categories of this paper are: eess
5
The categories of this paper are: gr-qc
27
The categories of this paper are: comp-gas