Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Towards Interpretable Multi-Task Learning Using Bilevel Programming   Interpretable Multi-Task Learning can be expressed as learning a sparse graph
of the task relationship based on the prediction performance of the learned
models. Since many natural phenomenon exhibit sparse structures, enforcing
sparsity on learned models reveals the underlying task relationship. Moreover,
different sparsification degrees from a fully connected graph uncover various
types of structures, like cliques, trees, lines, clusters or fully disconnected
graphs. In this paper, we propose a bilevel formulation of multi-task learning
that induces sparse graphs, thus, revealing the underlying task relationships,
and an efficient method for its computation. We show empirically how the
induced sparse graph improves the interpretability of the learned models and
their relationship on synthetic and real data, without sacrificing
generalization performance. Code at https://bit.ly/GraphGuidedMTL

23
The categories of this paper are: solv-int
31
The categories of this paper are: ao-sci
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
5
The categories of this paper are: gr-qc
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
19
The categories of this paper are: eess