Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Sparse Graphs for Prediction and Filtering of Multivariate Data
  Processes   We address the problem of prediction of multivariate data process using an
underlying graph model. We develop a method that learns a sparse partial
correlation graph in a tuning-free and computationally efficient manner.
Specifically, the graph structure is learned recursively without the need for
cross-validation or parameter tuning by building upon a hyperparameter-free
framework. Our approach does not require the graph to be undirected and also
accommodates varying noise levels across different nodes.Experiments using
real-world datasets show that the proposed method offers significant
performance gains in prediction, in comparison with the graphs frequently
associated with these datasets.

23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
30
The categories of this paper are: q-alg
1
The categories of this paper are: cs
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci
21
The categories of this paper are: adap-org
5
The categories of this paper are: gr-qc