NLFMBD 2014 - Workshop on New Learning Frameworks and Models for Big Data
Topics/Call fo Papers
Workshop on New Learning Frameworks and Models for Big Data
During ICML'2014
June 25, 2014, Beijing, China
Paper submission deadline: March 21, 2014
DESCRIPTION
-----------
Huge amounts of data are now easily and legally available on the Web.
This data is generally heterogeneous and merely structured. Machine
learning models which have been developed to automatically retrieve,
classify or cluster observations on large yet homogeneous data
collections have to be rethought. Indeed, many challenging problems,
inevitably associated to Big Data, have manifested the needs for
tradeoffs between the two conflicting goals of speed and accuracy. This
has led to some recent initiatives in both theory and practice and has
highly motivated the interest of the Machine Learning community. Further
theoretical challenges include how to tackle problems with large number
of target classes, appropriate optimization techniques to handle big
data problems. Structured/sequential prediction models for big data
problems such as prediction in hierarchy of classes has also gained
importance in recent years.
The goal of this workshop is to bring together research studies aiming
at developing new machine learning tools to handle new challenges
associated to Big Data mining. We are especially interested on the
following topics:
* Distributed on-line learning
* Multi-task learning for big data
* Transfer Learning for big data
* Optimization techniques for large-scale learning
* Handling large number of target classes in big data
* Structured prediction models in big data
* Speed/Accuracy tradeoffs in big data
* Statistical inference for big data
* Noise in Big data
We see the workshop as a venue for the presentation of papers focusing
on exploiting large scale data, but also as a forum for sharing ideas
across different application domains. In particular it is an opportunity
for discussion of techniques which are applicable to multiple types of
datasets.
SUBMISSION
----------
Please send to Massih-Reza.Amini-AT-imag.fr by email in PDF or postscript
in the ICML format the following:
- A full paper 6-10 pages
- In the body of your email, include (in plain ASCII): names of all
authors, their affiliations, their physical and email addresses and the
track number which corresponds to your submission.
Submissions will be reviewed for technical soundness, relevance,
significance and clarity by the organizing and review committee and
invitations to present will be sent accordingly.
The full paper should be formatted according to the standard ICML
templates available at: http://icml.cc/2014/icml2014stylefiles.zip and
then converted to pdf or postscript.
IMPORTANT DATES
---------------
Paper submission deadline: March 21, 2014
Notification of acceptance: April 18, 2014
Final camera ready submissions: May 5, 2014
Workshop: June 25, 2014
ORGANIZERS
-----------
- Massih-Reza Amini: Laboratoire d'Informatique de Grenoble, University
of Grenoble
- Rohit Babbar: Laboratoire d'Informatique de Grenoble, University of
Grenoble
- Éric Gaussier: Laboratoire d'Informatique de Grenoble, University of
Grenoble
- James Tin-Yau Kwok: Department of Computer Science and Engineering,
Hong Kong
- Ioannis Partalas: Laboratoire d'Informatique de Grenoble, University
of Grenoble
- Yiming Yang: School of Computer Science of Carnegie Mellon University
PUBLICATION
-----------
Accepted papers will be put into a proposal for a publication with
Cambridge Scholars.
During ICML'2014
June 25, 2014, Beijing, China
Paper submission deadline: March 21, 2014
DESCRIPTION
-----------
Huge amounts of data are now easily and legally available on the Web.
This data is generally heterogeneous and merely structured. Machine
learning models which have been developed to automatically retrieve,
classify or cluster observations on large yet homogeneous data
collections have to be rethought. Indeed, many challenging problems,
inevitably associated to Big Data, have manifested the needs for
tradeoffs between the two conflicting goals of speed and accuracy. This
has led to some recent initiatives in both theory and practice and has
highly motivated the interest of the Machine Learning community. Further
theoretical challenges include how to tackle problems with large number
of target classes, appropriate optimization techniques to handle big
data problems. Structured/sequential prediction models for big data
problems such as prediction in hierarchy of classes has also gained
importance in recent years.
The goal of this workshop is to bring together research studies aiming
at developing new machine learning tools to handle new challenges
associated to Big Data mining. We are especially interested on the
following topics:
* Distributed on-line learning
* Multi-task learning for big data
* Transfer Learning for big data
* Optimization techniques for large-scale learning
* Handling large number of target classes in big data
* Structured prediction models in big data
* Speed/Accuracy tradeoffs in big data
* Statistical inference for big data
* Noise in Big data
We see the workshop as a venue for the presentation of papers focusing
on exploiting large scale data, but also as a forum for sharing ideas
across different application domains. In particular it is an opportunity
for discussion of techniques which are applicable to multiple types of
datasets.
SUBMISSION
----------
Please send to Massih-Reza.Amini-AT-imag.fr by email in PDF or postscript
in the ICML format the following:
- A full paper 6-10 pages
- In the body of your email, include (in plain ASCII): names of all
authors, their affiliations, their physical and email addresses and the
track number which corresponds to your submission.
Submissions will be reviewed for technical soundness, relevance,
significance and clarity by the organizing and review committee and
invitations to present will be sent accordingly.
The full paper should be formatted according to the standard ICML
templates available at: http://icml.cc/2014/icml2014stylefiles.zip and
then converted to pdf or postscript.
IMPORTANT DATES
---------------
Paper submission deadline: March 21, 2014
Notification of acceptance: April 18, 2014
Final camera ready submissions: May 5, 2014
Workshop: June 25, 2014
ORGANIZERS
-----------
- Massih-Reza Amini: Laboratoire d'Informatique de Grenoble, University
of Grenoble
- Rohit Babbar: Laboratoire d'Informatique de Grenoble, University of
Grenoble
- Éric Gaussier: Laboratoire d'Informatique de Grenoble, University of
Grenoble
- James Tin-Yau Kwok: Department of Computer Science and Engineering,
Hong Kong
- Ioannis Partalas: Laboratoire d'Informatique de Grenoble, University
of Grenoble
- Yiming Yang: School of Computer Science of Carnegie Mellon University
PUBLICATION
-----------
Accepted papers will be put into a proposal for a publication with
Cambridge Scholars.
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Last modified: 2014-03-24 22:57:11