NIPS 2009 workshop on Learning from Multiple Sources with Applications to Robotics, Call for contributions


NIPS 2009 workshop on Learning from Multiple Sources with Applications to Robotics
Whistler, BC, Canada, December 11 or 12, 2009

Important Dates:

Submission of extended abstracts: October 27, 2009

Notification of acceptance: November 6, 2009

Workshop Description:

Learning from multiple sources denotes the problem of jointly learning from a set of (partially) related learning problems / views / tasks. This general concept underlies several subfields receiving increasing interest from the machine learning community, which differ in terms of the assumptions made about the dependency structure between learning problems. In particular, the concept includes topics such as data fusion, transfer learning, multitask learning, multiview learning, and learning under covariate shift. Several approaches for inferring and exploiting complex relationships between data sources have been presented, including both generative and discriminative approaches.

The workshop will provide a unified forum for cutting edge research on learning from multiple sources; the workshop will examine the general concept, theory and methods, and will also examine *robotics* as a natural application domain for learning from multiple sources. The workshop will address methodological challenges in the different subtopics and further interaction
between them. The intended audience is researchers working in fields of multi-modal learning, data fusion, and robotics.

(More detailed background information is available at the workshop website.)

The workshop includes a morning session focused on theory/methods, and an afternoon session focused on the robotics application.

The workshop is a core event of the PASCAL2 Network of Excellence.

PASCAL2 Invited Speakers:

Morning Session: Chris Williams – University of Edinburgh

Afternoon Session: to be announced

Submission Instructions:

We invite submission of extended abstracts to the workshop. Extended abstracts should be 2-4 pages, formatted in the NIPS style:
Unlike the main NIPS conference, identities of authors do not need to be removed from the extended abstracts.

Extended abstracts should be sent in .PDF or .PS file format by email, to either D.Hardoon (at) or gleen (at) Acceptance to the workshop will be determined based on peer
review of each extended abstract.

Submissions are expected to represent high-quality, novel contributions in theory/methods of learning from multiple sources,or high-quality, novel contributions in application of learning
from multiple sources to robotics (see below).

To encourage participants from the machine learning community to test their algorithms in the domain of robotics, we will make available a dataset, with computed features, representative of
open research issues in robotics. Robotics-oriented papers submitted to the workshop are strongly encouraged to contain an experimental evaluation on the database made available by the organizers. The obtained results will be presented by the organizers during the workshop.

Submitted extended abstracts may be accepted either as an oral presentation or as a poster presentation; there will be only a limited number of oral presentations in the morning and afternoon sessions.

Accepted extended abstracts will be made available online at the workshop website.

Depending on the quality of submissions, we will consider preparing a special issue of a journal or a collected volume on the topic of the workshop. A separate call for papers will then be issued after the workshop for the special issue/collected volume. Last year’s “Learning from Multiple Sources” workshop led to a special issue in Machine Learning (currently in progress).


* Barbara Caputo – Idiap Research Institute.
* Nicolò Cesa-Bianchi РUniversità degli Studi di Milan.
* David Hardoon – Institute for Infocomm Research (I2R).
* Gayle Leen – Helsinki University of Technology.
* Francesco Orabona – Idiap Research Institure.
* Jaakko Peltonen – Helsinki University of Technology.
* Simon Rogers – University of Glasgow.