Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Stochastic Learning for Sparse Discrete Markov Random Fields with
  Controlled Gradient Approximation Error   We study the $L_1$-regularized maximum likelihood estimator/estimation (MLE)
problem for discrete Markov random fields (MRFs), where efficient and scalable
learning requires both sparse regularization and approximate inference. To
address these challenges, we consider a stochastic learning framework called
stochastic proximal gradient (SPG; Honorio 2012a, Atchade et al.
2014,Miasojedow and Rejchel 2016). SPG is an inexact proximal gradient
algorithm [Schmidtet al., 2011], whose inexactness stems from the stochastic
oracle (Gibbs sampling) for gradient approximation - exact gradient evaluation
is infeasible in general due to the NP-hard inference problem for discrete MRFs
[Koller and Friedman, 2009]. Theoretically, we provide novel verifiable bounds
to inspect and control the quality of gradient approximation. Empirically, we
propose the tighten asymptotically (TAY) learning strategy based on the
verifiable bounds to boost the performance of SPG.

26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
3
The categories of this paper are: physics
25
The categories of this paper are: patt-sol
4
The categories of this paper are: cond-mat
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
24
The categories of this paper are: dg-ga
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph