Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Output Kernels for Multi-Task Problems   Simultaneously solving multiple related learning tasks is beneficial under a
variety of circumstances, but the prior knowledge necessary to correctly model
task relationships is rarely available in practice. In this paper, we develop a
novel kernel-based multi-task learning technique that automatically reveals
structural inter-task relationships. Building over the framework of output
kernel learning (OKL), we introduce a method that jointly learns multiple
functions and a low-rank multi-task kernel by solving a non-convex
regularization problem. Optimization is carried out via a block coordinate
descent strategy, where each subproblem is solved using suitable conjugate
gradient (CG) type iterative methods for linear operator equations. The
effectiveness of the proposed approach is demonstrated on pharmacological and
collaborative filtering data.

16
The categories of this paper are: stat
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
18
The categories of this paper are: econ
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
14
The categories of this paper are: math-ph
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
2
The categories of this paper are: math
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
8
The categories of this paper are: hep-ex