Connectionists: CFP: 1st Symposium on Advances in Approximate Bayesian Inference (AABI 2018)

[Thang Bui]

We invite researchers in machine learning and statistics to participate in
the:

1st Symposium on Advances in Approximate Bayesian Inference
Sunday December 2 2018, Montreal, Canada
www.approximateinference.org
Submission deadline: *19 October 2018*

*1. Call for Participation*

We invite researchers to submit their recent work on the development,
analysis, or application of approximate Bayesian inference. A submission
should take the form of an extended abstract of 2-4 pages in PDF format
using the PMLR one-column style [
http://approximateinference.org/pmlr/aabi_template.zip ]. For questions and
troubleshooting, visit CTAN [
https://ctan.org/tex-archive/macros/latex/contrib/jmlr ]. Author names do
not need to be anonymized and references may extend as far as needed beyond
the 4-page upper limit. If authors' research has previously appeared in a
journal, workshop, or conference (including the NIPS 2018 conference),
their symposium submission should extend that previous work. Submissions
may include a supplement/appendix, but reviewers are not responsible for
reading any supplementary material.

All submissions will be reviewed by at least three reviewers from the
field. Accepted submissions will be accepted to presentation only. The
authors of selected submissions will be invited to publish their paper in a
PMLR volume. We aim to keep a general inclusive nature of the symposium for
presentations. However, we will only invite the top-rated accepted papers
to be published through PMLR.

Papers should be submitted by 19 October through easychair [
https://easychair.org/conferences/?conf=aabi2018 ]. Final versions of the
symposium submissions are due by 1 December and will be posted on the
symposium website.

If you have any questions, please contact us at aabisymposium2018 at gmail.com.

*2. Symposium Overview*

Probabilistic modeling is a useful tool to analyze and understand
real-world data. Central to the success of Bayesian modeling is
posterior inference,
for which approximate inference algorithms are typically needed in most
problems of interest. The two pillars of approximate Bayesian inference are
variational and Monte Carlo methods. In recent years, there have been
numerous advances in both methods, which have enabled Bayesian inference in
increasingly challenging scenarios involving complex probabilistic models
and large datasets.

In this symposium, besides recent advances in approximate inference, we
will discuss the impact of Bayesian inference, connecting approximate inference
methods with other fields. In particular, we encourage submissions that
relate Bayesian inference to the fields of reinforcement learning, causal
inference, decision processes, Bayesian compression, or differential
privacy, among others. We also encourage submissions that contribute to
connecting different approximate inference methods, such as variational
inference and Monte Carlo.

This symposium can be seen as a continuation of previous workshops at NIPS:
+ NIPS 2017 Workshop: Advances in Approximate Bayesian Inference
+ NIPS 2016 Workshop: Advances in Approximate Bayesian Inference
+ NIPS 2015 Workshop: Advances in Approximate Bayesian Inference
+ NIPS 2014 Workshop: Advances in Variational Inference

*3. Key Dates*

Paper submission: *19 October 2018 (11:55pm GMT)*
Acceptance notification: 13 November 2018
Final paper submission: 1 December 2018


Symposium organizers:
Cheng Zhang (Microsoft Research)
Dawen Liang (Netflix)
Francisco Ruiz (University of Cambridge / Columbia University)
Thang Bui (University of Sydney)

Advisory committee:
Christian Robert (Université Paris Dauphine / University of Warwick)
David Blei (Columbia University)
Dustin Tran (Google Brain / Columbia University)
James McInerney (Spotify)
Stephan Mandt (University of California Irvine)
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