Connectionists: CFP: NIPS-2016: Adaptive and Scalable Nonparametric Methods in ML workshop

[Zoltan Szabo]

Apologies for cross-posting.

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CALL FOR PAPERS

Adaptive and Scalable Nonparametric Methods in ML workshop @ NIPS-2016
December 10th, 2016 
Barcelona, Spain
https://sites.google.com/site/nips2016adaptive/

Important dates:

Submission deadline: Sept. 23, 2016.
Acceptance notification: Oct. 10, 2016.

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Description:

Large amounts of high-dimensional data are routinely acquired in
scientific fields ranging from biology, genomics and health sciences to
astronomy and economics due to improvements in engineering and data
acquisition techniques. Nonparametric methods allow for better
modelling of complex systems underlying data generating processes
compared to traditionally used linear and parametric models. From
statistical point of view, scientists have enough data to reliably fit
nonparametric models. However, from computational point of view,
nonparametric methods often do not scale well to big data problems.

The aim of this workshop is to bring together practitioners, who are
interested in developing and applying nonparametric methods in their
domains, and theoreticians, who are interested in providing sound
methodology. We hope to effectively communicate advances in development
of  computational tools for fitting nonparametric models and discuss
challenging future directions that prevent applications of
nonparametric methods to big data problems.

We encourage submissions on a variety of topics, including but not
limited to:

- Randomized procedures for fitting nonparametric models. For example,
  sketching, random projections, core set selection, etc.
- Nonparametric probabilistic graphical models 
- Scalable nonparametric methods 
- Multiple kernel learning
- Random feature expansion
- Novel applications of nonparametric methods
- Bayesian nonparametric methods
- Nonparametric network models

This workshop is a fourth in a series of NIPS workshops on modern
nonparametric methods in machine learning. Previous workshops focused
on time/accuracy tradeoffs, high dimensionality and dimension reduction
strategies, and automating the learning pipeline.

Submission: 

Papers submitted to the workshop should be up to four pages
long (including references), extended abstracts in camera-ready format
using the NIPS style. They should be uploaded (.pdf, up to 5MB) to CMT
(https://cmt.research.microsoft.com/ADAPTIVE2016). Accepted submissions
will be presented as talks or posters.

Format: 

The workshop will be a one day workshop. As with last year's
workshop, the workshop will consist of 6-8 invited and contributed
talks, with a poster session.

Confirmed speakers:

- Arthur Gretton (University College London)
- David Dunson (Duke University)
- Francis Bach (INRIA, ENS)
- Ming Yuan (University of Wisconsin-Madison)
- Olga Klopp (CNRS)
- Richard Samworth (University of Cambridge)

Organizers:

- Aaditya Ramdas (UC Berkeley)
- Bharath K. Sriperumbudur (Pennsylvania State University)
- Han Liu (Princeton University)
- John Lafferty (University of Chicago)
- Mladen Kolar (University of Chicago Booth School of Business)
- Samory Kpotufe (Princeton University)
- Zoltan Szabo (University College London)