Connectionists: CFP: NIPS workshop on Learning in High Dimensions with Structure

[Nikhil Rao]

CALL FOR PAPERS

NIPS 2016 Workshop on Learning in High Dimensions with Structure
Barcelona, Spain
Website: https://sites.google.com/site/structuredlearning16/

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IMPORTANT DATES:
September 23 : Extended abstracts due
October   4  : Notification of acceptance
November  22 : Final papers due
December  9  : Workshop



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Several applications necessitate learning a very large number of parameters
from small amounts of data, which can lead to overfitting, statistically
unreliable answers, and large training/prediction costs.  A common and
effective method to avoid the above mentioned issues is to restrict the
parameter-space using specific structural constraints such as sparsity or
low rank. However, such simple constraints do not fully exploit the richer
structure which is available in several applications and is present in the
form of correlations, side information or higher order structure. Designing
new structural constraints requires close collaboration between domain
experts and machine learning practitioners. Similarly, developing efficient
and principled algorithms to learn with such constraints requires further
collaborations between experts in diverse areas such as statistics,
optimization, approximation algorithms etc. This interplay has given rise
to a vibrant research area.

The main objective of this workshop is to consolidate current ideas from
diverse areas such as machine learning, signal processing, theoretical
computer science, optimization and statistics, clarify the frontiers in
this area, discuss important applications and open problems, and foster new
collaborations.


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INVITED SPEAKERS

Amir Beck (Technion)
Sham Kakade (U. Washington)
Po-Ling Loh (U. Wisconsin - Madison)
Guillaume Obozinski (Ecole des Ponts)
Rene Vidal (Johns Hopkins)
Allen Yang (UC Berkeley)
Rob Nowak (U. Wisconsin - Madison)
Richard Samworth (Cambridge)

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SUBMISSION INSTRUCTIONS

We invite submissions in the form of 2 page extended abstracts, excluding
references, in PDF format and in NIPS style. If accepted, final submissions
may be at most 4 pages long, excluding references and supplementary
materials.  Submissions will be accepted as poster presentations, and will
be published on the workshop website.

The authors may choose to make their identities visible on the submissions.
Submissions should be mailed to nips.lhds at gmail.com. Topics of interest
include, but are not limited to:
- Algorithms
- Online and Reinforcement learning in high dimensions
- Submodularity for high dimensional structured learning
- Novel regularization frameworks
- Theory
- Applications, including machine learning, speech and signal processing,
computer vision and biostatistics.


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ORGANIZERS

Nikhil Rao (Technicolor)
Prateek Jain (Microsoft)
Hsiang-Fu Yu (Amazon)
Francis Bach (Ecole Normale Superieure)
Ming Yuan (UW Madison)
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