Connectionists: CFP: NIPS Workshop on Advances in Approximate Bayesian Inference

[franrruiz87]

Call For Papers
NIPS Workshop on Advances in Approximate Bayesian Inference
Friday, 8th December 2017, Long Beach, California
URL: http://approximateinference.org

Submission deadline: Nov 01, 2017
Please direct questions to: aabiworkshop2017 at gmail.com

## 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 NIPS style. 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 NIPS 2017), their workshop submission 
should extend that previous work. Submissions may include a 
supplement/appendix, but reviewers are not responsible for reading any 
supplementary material.

This year, the workshop offers multiple best paper awards. They are open 
to all researchers, and a few awards are restricted to junior 
researchers. Submitting by the deadline automatically entitles you for 
consideration for all of the following:

+ Roughly $3000 in total, to be allocated across winners
+ Four NIPS 2017 workshop registration fee waivers

## Abstract

Approximate inference is key to modern probabilistic modeling. Thanks to 
the availability of big data, significant computational power, and 
sophisticated models, machine learning has achieved many breakthroughs 
in multiple application domains. At the same time, approximate inference 
becomes critical since exact inference is intractable for most models of 
interest. Within the field of approximate Bayesian inference, 
variational and Monte Carlo methods are currently the mainstay 
techniques. For both methods, there has been considerable progress both 
on the efficiency and performance.

In this workshop, we encourage submissions advancing approximate 
inference methods. We are open to a broad scope of methods within the 
field of Bayesian inference. In addition, we also encourage applications 
of approximate inference in many domains, such as computational biology, 
recommender systems, differential privacy, and industry applications.

## Key Dates

Nov 01, 2017: Submission Deadline
Nov 15, 2017: Notification of Acceptance
Nov 24, 2017: Submission Reviews & Award Notifications

## Organizers

Francisco Ruiz, Stephan Mandt, Cheng Zhang, James Mclnerney, Dustin Tran

## Advisory Committee

Tamara Broderick, Michalis Titsias, David Blei, Max Welling