AABI2017: Advances in Approximate Bayesian Inference Long Beach, CA, United States, December 8, 2017 |
Conference website | http://approximateinference.org |
Submission link | https://easychair.org/conferences/?conf=aabi2017 |
Submission deadline | November 1, 2017 |
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@gmail.com
Call For Participation And Submission Instructions
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.
Please submit via our easychair website. The submission deadline is November 1st, 2017.
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:
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Roughly $3000 in total, to be allocated across winners
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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
Committees
Organizing Committee
- Francisco J. R. Ruiz (Columbia University)
- Stephan Mandt (Disney Research)
- Cheng Zhang (Disney Research)
- James McInerney (Spotify)
- Dustin Tran (Columbia University)
Advisory Committee
- Tamara Broderick (MIT)
- Michalis Titsias (Athens University of Economics and Business)
- David Blei (Columbia University)
- Max Welling (University of Amsterdam)
Venue
The workshop will be held in Long Beach Convention Center (Long Beach, CA, USA).