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 aabisymposium2...@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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