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

[Carlos Soares]

Begin forwarded message:

Date: 14 de Outubro de 2014 às 16:30:13 WEST

From: "'Zoltan Szabo' via Machine Learning News" <ml-...@googlegroups.com>

To: 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

https://sites.google.com/site/nips2014modernnonparametric/

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Overview:

-------------

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

sizes’.

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.

Submission:

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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

Registration: 

-----------------

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
http://www.gatsby.ucl.ac.uk/~szabo/

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