Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Probable convexity and its application to Correlated Topic Models   Non-convex optimization problems often arise from probabilistic modeling,
such as estimation of posterior distributions. Non-convexity makes the problems
intractable, and poses various obstacles for us to design efficient algorithms.
In this work, we attack non-convexity by first introducing the concept of
\emph{probable convexity} for analyzing convexity of real functions in
practice. We then use the new concept to analyze an inference problem in the
\emph{Correlated Topic Model} (CTM) and related nonconjugate models. Contrary
to the existing belief of intractability, we show that this inference problem
is concave under certain conditions. One consequence of our analyses is a novel
algorithm for learning CTM which is significantly more scalable and qualitative
than existing methods. Finally, we highlight that stochastic gradient
algorithms might be a practical choice to resolve efficiently non-convex
problems. This finding might find beneficial in many contexts which are beyond
probabilistic modeling.

11
The categories of this paper are: hep-lat
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
2
The categories of this paper are: math
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys
8
The categories of this paper are: hep-ex
0
The categories of this paper are: hep-ph