Fwd: [ML-news] CFP: Deadline extension (Oct. 31): Modern Nonparametrics 3 @ NIPS

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Carlos Soares

Oct 17, 2014, 11:52:48 AM10/17/14
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Date: 14 de Outubro de 2014 às 16:30:13 WEST
From: "'Zoltan Szabo' via Machine Learning News" <ml-...@googlegroups.com>
Subject: [ML-news] CFP: Deadline extension (Oct. 31): Modern Nonparametrics 3 @ NIPS
Reply-To: Zoltan Szabo <zoltansz...@yahoo.co.uk>

==================DEADLINE EXTENSION============
==                            Submission open until Oct. 31                  ==
==                              [Apology for cross-postings]                   ==

Call for Papers: Modern Nonparametrics 3: Automating the Learning Pipeline
held in conjunction with Neural Information Processing Systems (NIPS 2014)

December 13, 2014, Montreal, Canada



Nonparametric methods (kernel methods, kNN, classification trees, etc) are
designed to handle complex pattern recognition problems.  Such complex
problems arise in modern applications such as genomic experiments, climate
analysis, robotic control, social network analysis, and so forth. 
There is a growing need for statistical procedures that can be used
“off-the-shelf”, i.e. procedures with as few parameters as possible, or
better yet, procedures which can “self-tune” to a particular application
at hand.

In traditional statistics, much effort has gone into so called
“adaptive” procedures which can attain optimal risks over large sets of
models of increasing complexity. Examples are model selection approaches
based on penalized empirical risk minimization, approaches based on
stability of estimates (e.g. Lepski’s methods), thresholding approaches
under sparsity assumptions, and model averaging approaches. Most of these
approaches rely on having tight bounds on the risk of learning procedures
(under any parameter setting), hence other approaches concentrate on tight
estimations of the actual risks, e.g., Stein’s risk estimators,
bootstrapping methods, data dependent learning bounds.

In theoretical machine learning, much of the work has focused on proper
tuning of the actual optimization procedures used to minimize (penalized)
empirical risks. In particular, great effort has gone into the automatic
setting of important tuning parameters such as ‘learning rates’ and ‘step

Another approach out of machine learning arises in the kernel literature
under the name of ‘automatic representation learning’. The aim of the
approach, similar to theoretical work on model selection, is to
automatically learn an appropriate (kernel) transformation of the data for
use with kernel methods such as SVMs or Gaussian processes.

A main aim of this workshop is to cover the various approaches proposed so
far towards automating the learning pipeline, and the practicality of these
approaches in light of modern constraints. We are particularly interested
in understanding whether large datasizes and dimensionality might
help the automation effort since such datasets in fact provide more
information on the patterns being learned.

This workshop is third in a series of NIPS workshops on modern
nonparametric methods in machine learning, which several of the present
organizers were involved in running during NIPS 2013 and NIPS 2012 (see
organizer biographies). These previous workshops focused on the challenges
posed by large data sizes (e.g. time/accuracy tradeoffs) and large
dimensionality (e.g. dimension reduction strategies). The main focus of the
present workshop, automating the learning pipeline, builds on these
previous workshops.


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 sent by email to ''nonparametr...@gmail.com''.
Accepted submissions will be presented as talks or posters.

Important Dates:

submission deadline: Oct. 31, 2014 (23:59 UTC)
notification of acceptance: Nov. 10, 2014 (23:59 UTC)
workshop: Dec. 13, 2014


Participants should refer to the NIPS-2014 website for information on how to
register for the workshop.

Zoltan Szabo
Gatsby Computational Neuroscience Unit
University College London

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