Workshop
Advances in Approximate Bayesian Inference
Tamara Broderick · Stephan Mandt · James McInerney · Dustin Tran · David Blei · Kevin Murphy · Andrew Gelman · Michael I Jordan

Fri Dec 9th 08:00 AM -- 06:30 PM @ Room 112
in Friday Workshops »
Event URL: http://approximateinference.org »

Bayesian analysis has seen a resurgence in machine learning, expanding its scope beyond traditional applications. Increasingly complex models have been trained with large and streaming data sets, and they have been applied to a diverse range of domains. Key to this resurgence has been advances in approximate Bayesian inference. Variational and Monte Carlo methods are currently the mainstay techniques, where recent insights have improved their approximation quality, provided black box strategies for fitting many models, and enabled scalable computation.

In this year's workshop, we would like to continue the theme of approximate Bayesian inference with additional emphases. In particular, we encourage submissions not only advancing approximate inference but also regarding (1) unconventional inference techniques, with the aim to bring together diverse communities; (2) software tools for both the applied and methodological researcher; and (3) challenges in applications, both in non-traditional domains and when applying these techniques to advance current domains.

08:30 AM Introduction (Presentation)
08:35 AM Invited talk 1 (Presentation)
09:00 AM Contributed talk 1 (Presentation)
09:15 AM Invited talk 2 (Presentation)
09:40 AM Panel on Advances in Software for Approximate Bayesian Inference (Discussion Panel)
11:00 AM Contributed talk 2 (Presentation)
11:15 AM Poster spotlights (Presentation)
11:35 AM Poster session (Poster presentations)
02:10 PM Invited talk 3 (Presentation)
03:30 PM Contributed talk 3 (Presentation)
03:45 PM Invited talk 4 (Presentation)
04:10 PM Contributed talk 4 (Presentation)
04:25 PM Panel On the Foundations and Future of Approximate Inference (Discussion Panel)

Author Information

Tamara Broderick (MIT)
Stephan Mandt (Disney Research)
James McInerney (Spotify Research)
Dustin Tran (Google Brain)
David Blei (Columbia University)

David Blei is a Professor of Statistics and Computer Science at Columbia University, and a member of the Columbia Data Science Institute. His research is in statistical machine learning, involving probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference algorithms for massive data. He works on a variety of applications, including text, images, music, social networks, user behavior, and scientific data. David has received several awards for his research, including a Sloan Fellowship (2010), Office of Naval Research Young Investigator Award (2011), Presidential Early Career Award for Scientists and Engineers (2011), Blavatnik Faculty Award (2013), and ACM-Infosys Foundation Award (2013). He is a fellow of the ACM.

Kevin Murphy (Google)
Andrew Gelman (Columbia University)
Michael I Jordan (University of California, Berkeley)

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