Call for Participation 

We invite researchers in machine learning and statistics to participate in the: 

NIPS 2015 Workshop on Advances in Approximate Bayesian Inference 
11 December 2015, Montreal, Canada 
www.approximateinference.org 
Submission deadline: 2 October 2015 

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 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 the NIPS 2015 conference), their workshop submission should extend that previous work. Submissions may include a supplement/appendix, but reviewers are not responsible for reading any supplementary material. 

Submissions will be accepted either as contributed talks or poster presentations. Extended abstracts should be submitted by 2 October; see website for submission details. Final versions of the extended abstract are due by 1 December, and will be posted on the workshop website. 

2. Workshop Overview 

The ever-increasing size of data sets has resulted in an immense effort in Bayesian statistics to develop more expressive probabilistic models. Inference remains a challenge and limits the use of these models in large-scale scientific and industrial applications. Thus we must resort to approximate inference, which is computationally efficient on massive and streaming data—without compromising on the complexity of these models. This workshop aims to bring together researchers and practitioners in order to discuss recent advances in approximate inference; we also aim to discuss the methodological and foundational issues in such techniques in order to consider future improvements. 

The resurgence of interest in approximate inference has furthered development in many techniques: for example, scalability, black box techniques, and dependency in variational inference; divide and conquer techniques in expectation propagation; dimensionality reduction using random projections; and stochastic variants of Laplace approximation-based methods. Despite this interest, there remain significant trade-offs in speed, accuracy, generalizability, and learned model complexity. In this workshop, we will discuss how to rigorously characterize these tradeoffs, as well as how they might be made more favourable. Moreover, we will address the issues of its adoption in scientific communities which could benefit from advice on their practical usage and the development of relevant software packages. 

This workshop is motivated by, and in some ways a successor to, the NIPS 2014 workshop on Advances in Variational Inference. It is supported by the International Society of Bayesian Analysis (ISBA) and Adobe Creative Technologies Laboratory. 

3. Speakers and Panelists 

Invited Speakers 
John Cunningham (Columbia University) 
Emily Fox (University of Washington) 
Rajesh Ranganath (Princeton University) 
James Hensman (University of Sheffield) 
Andrea Montanari (Stanford University) 

Panel: "Tricks of the Trade" 
Matt Hoffman (Adobe Research) 
Danilo Rezende (Google DeepMind) 
David Duvenaud (Harvard University) 
Alp Kucukelbir (Columbia University) 
Stephan Mandt (Columbia University) 

Panel: "On the Foundations and Future of Approximate Inference" 
Max Welling (University of Amsterdam) 
Yee Whye Teh (University of Oxford) 
Andrew Gelman (Columbia University) 
Steve MacEachern (Ohio State University) 
Manfred Opper (Technische Universität Berlin) 

4. Key Dates 

Paper submission: 2 October 2015 
Travel award application deadline: 2 October 2015 
Acceptance notification: 23 October 2015 
Travel award notification: 30 October 2015 
Notification of type of presentation: 5 November 2015 
Final paper submission: 1 December 2015 
Workshop: 11 December 2015 

Workshop Organizers 
Dustin Tran (Harvard University)     
Tamara Broderick (MIT) 
Shakir Mohamed (Google DeepMind) 
Alp Kucukelbir (Columbia University) 
Stephan Mandt (Columbia University) 
James McInerney (Columbia University) 
Matt Hoffman (Adobe Research) 
David Blei (Columbia University) 
Neil Lawrence (University of Sheffield)

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