NIPS 2017 Workshop on Approximate Inference

This year we’re organizing the third NIPS workshop on approximate inference. It is together with Francisco Ruiz, Stephan Mandt, Cheng Zhang, and James Mclnerney—and alongside our amazing committee of Tamara Broderick, Michalis Titsias, David Blei, and Max Welling.

Call for papers below.

Note: We have a lot of funding for awards this year. We’ve decided to not only allocate some funding for Ph.D. students and early postdocs, but we also feature a best paper award. Submit your papers!

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

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 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