Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sparse coding for multitask and transfer learning   We investigate the use of sparse coding and dictionary learning in the
context of multitask and transfer learning. The central assumption of our
learning method is that the tasks parameters are well approximated by sparse
linear combinations of the atoms of a dictionary on a high or infinite
dimensional space. This assumption, together with the large quantity of
available data in the multitask and transfer learning settings, allows a
principled choice of the dictionary. We provide bounds on the generalization
error of this approach, for both settings. Numerical experiments on one
synthetic and two real datasets show the advantage of our method over single
task learning, a previous method based on orthogonal and dense representation
of the tasks and a related method learning task grouping.

30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
18
The categories of this paper are: econ
8
The categories of this paper are: hep-ex
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
32
The categories of this paper are: atom-ph
5
The categories of this paper are: gr-qc
36
The categories of this paper are: cmp-lg
9
The categories of this paper are: nlin
28
The categories of this paper are: alg-geom