Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Vector-Space Markov Random Fields via Exponential Families   We present Vector-Space Markov Random Fields (VS-MRFs), a novel class of
undirected graphical models where each variable can belong to an arbitrary
vector space. VS-MRFs generalize a recent line of work on scalar-valued,
uni-parameter exponential family and mixed graphical models, thereby greatly
broadening the class of exponential families available (e.g., allowing
multinomial and Dirichlet distributions). Specifically, VS-MRFs are the joint
graphical model distributions where the node-conditional distributions belong
to generic exponential families with general vector space domains. We also
present a sparsistent $M$-estimator for learning our class of MRFs that
recovers the correct set of edges with high probability. We validate our
approach via a set of synthetic data experiments as well as a real-world case
study of over four million foods from the popular diet tracking app
MyFitnessPal. Our results demonstrate that our algorithm performs well
empirically and that VS-MRFs are capable of capturing and highlighting
interesting structure in complex, real-world data. All code for our algorithm
is open source and publicly available.

2
The categories of this paper are: math
37
The categories of this paper are: supr-con
31
The categories of this paper are: ao-sci
0
The categories of this paper are: hep-ph
1
The categories of this paper are: cs
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
26
The categories of this paper are: bayes-an