CFP: ECML-PKDD workshop on Collective Learning and Inference on Structured Data

[Sriraam Natarajan]

 Apologies if you receive multiple copies.

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CFP - CoLISD2012 - Submissions deadline: 29th June 2012
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Call for Papers:  CoLISD: Collective Learning and Inference on Structured Data

Workshop at ECML-PKDD 2012

Important Dates

*****Workshop paper submission deadline: June 29, 2012
*****Workshop paper acceptance notification: July 20, 2012
*****Workshop paper camera-ready deadline: Aug 3, 2012

Topic

Classical ML techniques assume the data to be iid, but the real world
data is inherently relational and can generally be represented using
graphs or some variants of them. The importance of modelling
structured  data is evident from its increasing presence: WWW, social
networks, organizational network, image,  protein sequence, relational
data etc. This field has been recently receiving a lot of attention in
the community under different themes depending on the problem
addressed and the nature of solution.  Researchers in different areas
have proposed very useful and successful frameworks:

* Iterative collective classification involves use of a local
classifier that embeds the node's own attributes and neighbours
information in a feature vector, and classifies the nodes in an
iterative procedure.

* Statistical relational learning combines statistics (uncertainty)
and relational information (first order logic) to model the target
domain.
Structured prediction involves machine learning techniques for
structured objects which embeds the relationship between output
classes.

* Regularization based framework poses the local smoothness constraint
on the structured data i.e., uses it as a constraint or
side-information.

* Kernel methods for structured data deals with developing similarity
functions for objects of a domain that can handle relationship between
objects, for example a tree, as well as heterogeneous
representations.

* Message passing techniques such as belief propagation, loopy belief
propagation, mean field relaxation labelling on networks.

Learning with network data involves many building blocks:

* How to build the relationship network.
* How to learn the prediction model (classification/regression) with
fully supervised or partially supervised data.
* How to use the learnt model for inference on fully unlabeled or
partially labelled data.
* How to manipulate the relational information to reduce the
computational cost, such as lifted  probabilistic inference in SRL.

There is huge research progress on these subtasks in each area
individually. Also, workshops are held for  each field such as SRL,
ILP, StarAI, GBR, GDM. Collective inference is a common factor to all
these  subtasks, and notably it has attained huge progress in
individual areas. Some of the future/past workshops on collective
inference include "CVPR WS on Inference in Graphical Models and
Structured  Potentials", "Propagation Algorithms on Graphs with
Cycles: Theory and Applications", "Approximate inference-How far have
we come?", "Approximate Learning of Large Scale Graphical Models:
Theory and Applications". We believe the current situation provides us
with an opportunity for attempts at synthesis, forming a common core
of problems and ideas, and cross-pollinating across subareas. There
have been  few attempts, and a notable success in the MLG series. MLG
addresses all general aspects of mining and
learning with graphs, whereas CoLISD focuses on the within-network
learning and inference tasks with  special emphasis on collective
inference. Inspired by the success of MLG, this workshop will attempt
to reach out to different groups which work on the same theme and to
explore together how to reach the goals w.r.t within-network learning
and inference for each subfield mentioned above.

Following up on the successful conduct of the CoLISD workshop last
year at ECML PKDD, we propose to organize the second edition of the
workshop this year. The first edition of the workshop succeeded in
bringing together researchers from various communities who look at
different aspects of learning with structured data. For many of the
participants it was the first time they were seriosuly looking at
approaches from other disciplines. The wide spread feeling was that
the workshop should be continued since there was scope for much
crossfertilization. We are currently working on a special issue of MLJ
based on the outcome of the first workshop.

Technical original research papers and position papers (12 pages LNCS)
on each of the topics are invited for submission on or before 29th
June 2012.

Potential topics include (but are not limited to):
   * Collective Learning
   * Collective inference
   * Representation of structured data : for example, embedding a
node's attributes and neighboring nodes' class distribution
information as a flat vector
   * Cross-domain applications
   * Comparison study aimed at exploring commonality and differences
between topics mentioned above

Invited Speakers
Charles Sutton, University of Edinburgh
Sebastian Reidel, University of Massachusetts, Amherst
Christopher Ré, University of Wisconsin-Madison

Submission Instructions
The papers must be written in English and formatted according to the
Springer-Verlag Lecture Notes in Artificial Intelligence guidelines.
Authors instructions and style files can be downloaded at:
http://www.springer.de/comp/lncs/authors.html. The maximum length of
papers is 12 pages in this format. Submissions must be made through
EasyChair system at
https://www.easychair.org/conferences/?conf=colisd2012.

Organisers
 * Balaraman Ravindran, Dept of CSE, IIT Madras
 * Kristian Kersting, Dept. of Knowledge Discovery , Fraunhofer IAIS
 * Sriraam Natarajan, Wake Forest University Baptist Medical Center

Local Organiser
 * S. Shivashankar, Ericsson R&D, Chennai

Program Committee
Annalisa Appice, Università degli Studi di Bari
Mustafa Bilgic, Illinois Institute of Technology
Joschka Boedecker, Osaka University
Ulf Brefeld, Yahoo Research, Barcelona, Spain
Michelangelo Ceci, Università degli Studi di Bari
Janardhan Rao Doppa, Oregon State University
Saket Joshi, Oregon State University
Sofus A. Macskassy, University of Southern California
Oliver Obst, CSIRO ICT Centre
Scott Sanner, Australian National University