Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distributed Self-Paced Learning in Alternating Direction Method of
  Multipliers   Self-paced learning (SPL) mimics the cognitive process of humans, who
generally learn from easy samples to hard ones. One key issue in SPL is the
training process required for each instance weight depends on the other samples
and thus cannot easily be run in a distributed manner in a large-scale dataset.
In this paper, we reformulate the self-paced learning problem into a
distributed setting and propose a novel Distributed Self-Paced Learning method
(DSPL) to handle large-scale datasets. Specifically, both the model and
instance weights can be optimized in parallel for each batch based on a
consensus alternating direction method of multipliers. We also prove the
convergence of our algorithm under mild conditions. Extensive experiments on
both synthetic and real datasets demonstrate that our approach is superior to
those of existing methods.

35
The categories of this paper are: mtrl-th
21
The categories of this paper are: adap-org
9
The categories of this paper are: nlin
18
The categories of this paper are: econ
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
20
The categories of this paper are: acc-phys
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
23
The categories of this paper are: solv-int