CALL FOR PAPERS
NIPS 2016 Workshop:
Practical Bayesian Nonparametrics
December 9 or 10, 2016 (date to be confirmed)
Barcelona, Spain
https://sites.google.com/site/nipsbnp2016/
<https://sites.google.com/site/nipsbnp2016/>
Because of the NIPS early registration deadline this year, we will have two
rounds of submission. Papers submitted to the first round will receive a
decision before the early registration deadline. Authors may submit any number
of papers to both rounds.
Important Dates:
Round 2 Submission Deadline: November 10, 2016
Round 2 Acceptance Notification: November 18, 2016
Presentation Type (Poster/Oral) and Travel Award Notification: November 18,
2016
Final Paper Due: December 1, 2016
=======================================================
In theory, Bayesian nonparametric (BNP) methods are well suited to the
large data sets that arise in the sciences, technology, politics, and other
applied fields. By making use of infinite-dimensional mathematical structures,
BNP methods allow the complexity of a learned model to grow as the size
of a data set grows, exhibiting desirable Bayesian regularization properties
for small data sets and allowing the practitioner to learn ever more from
larger
data sets. These properties have resulted in the adoption of BNP methods
across a diverse set of application areas---including, but not limited to,
biology,
neuroscience, the humanities, social sciences, economics, and finance.
In practice, BNP methods present a number of computational and modeling
challenges. Recent work has brought a wide range of models to bear on
applied problems, going beyond the Dirichlet process and Gaussian process.
Meanwhile, advances in accelerated inference are making these models
tractable in big data problems.
In this workshop, we focus on the practical aspects of BNP.
We will explore new BNP methods for diverse applied problems, including
cutting-edge models and inference algorithms being developed
by application domain experts. We invite paper submissions that will be
presented in either oral or poster form. We welcome submissions from
the following list of areas:
* The application of Bayesian nonparametric methods to new application domains.
* Inference algorithms for BNP, including MCMC, SMC, variational methods, and
others.
* Novel Bayesian nonparametric models for new or existing problems.
* Software packages that allow the widespread use of BNP methods,
including probabilistic programming languages and implementations of
general-purpose inference algorithms.
A major focus of the workshop will be to expose participants to practical
software
tools for performing Bayesian nonparametric analyses. In particular, we will
host
hands-on tutorials to introduce workshop participants to some of the
software packages that can be used to easily perform posterior inference
for BNP models: Stan, BNPy, and BNP.jl. There will also be two panel
discussions---one each on applications and software---focused on practical
issues underlying applying BNP methods to real-world problems.
We will bring together participants from a variety of fields, including machine
learning, statistics, engineering, political science, and various biological
sciences. The workshop will be relevant both to BNP experts as well as
those interested in learning how to apply BNP models. There will be a special
emphasis on work that makes BNP methods easy-to-use in practice and
computationally efficient. Participants will leave the workshop with (i)
exposure
to recent advances in the field, (ii) hands-on experience with software
implementing BNP methods, and (iii) an idea of the current challenges that
need to be overcome in order to make BNP methods more widespread in practice.
Invited Speakers:
Tamara Broderick (MIT)
Bailey Fosdick (Colorado State University)
Maria DeYoreo (Duke University)
Marc Deisenroth (Imperial College London)
Jennifer Hill (New York University)
Invited Panelists (Applications):
Bailey Fosdick (Colorado State University)
Maria DeYoreo (Duke University)
Suchi Saria (Johns Hopkins University)
Jim Griffin (University of Kent)
Marc Deisenroth (Imperial College London)
Jennifer Hill (New York University)
Invited Panelists (Software):
Mike Hughes, Harvard University, lead dev on BNPy
Martin Trapp, Austrian Research Institute for Artificial Intelligence, lead dev
on BNP.jl
Dustin Tran, Columbia University, lead dev on Edward and Stan contributor
Stan Representative
Submissions:
Submissions are solicited in 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 2016 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. There will be no published proceedings for this
workshop; we hope that authors will find discussion and feedback at
the workshop beneficial for developing the research they present, and
we encourage authors to submit their resulting work for publication in
other venues after the workshop.
Extended abstracts will be submitted in two rounds due to the NIPS early
registration deadline this year. The deadline for the second round is
November 10, 2016. Travel awards for students and young researchers
will be awarded for the best contributed work in the form of free workshop
registration.
Workshop Organizers:
Tamara Broderick (MIT)
Trevor Campbell (MIT)
Nick Foti (University of Washington)
Mike Hughes (Harvard)
Jeff Miller (Harvard)
Aaron Schein (University of Massachusetts Amherst)
Sinead Williamson (University of Texas at Austin)
Yanxun Xu (Johns Hopkins University)
Advisory Committee:
Emily Fox (University of Washington)
Antonio Lijoi (University of Pavia)
Fernando Quintana (Pontificia Universidad Catolica de Chile)
Erik Sudderth (Brown University)
Hanna Wallach (Microsoft Research)
Sponsors:
International Society for Bayesian Analysis (ISBA)
Stan
Stan Group
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