Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Faster Stochastic Variational Inference using Proximal-Gradient Methods
  with General Divergence Functions   Several recent works have explored stochastic gradient methods for
variational inference that exploit the geometry of the variational-parameter
space. However, the theoretical properties of these methods are not
well-understood and these methods typically only apply to
conditionally-conjugate models. We present a new stochastic method for
variational inference which exploits the geometry of the variational-parameter
space and also yields simple closed-form updates even for non-conjugate models.
We also give a convergence-rate analysis of our method and many other previous
methods which exploit the geometry of the space. Our analysis generalizes
existing convergence results for stochastic mirror-descent on non-convex
objectives by using a more general class of divergence functions. Beyond giving
a theoretical justification for a variety of recent methods, our experiments
show that new algorithms derived in this framework lead to state of the art
results on a variety of problems. Further, due to its generality, we expect
that our theoretical analysis could also apply to other applications.

9
The categories of this paper are: nlin
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
13
The categories of this paper are: quant-ph
32
The categories of this paper are: atom-ph
34
The categories of this paper are: chem-ph
21
The categories of this paper are: adap-org