[Apologies for cross-postings]
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LMCE 2014 # First International Workshop on Learning over Multiple
Contexts @ ECML 2014
Generalization and reuse of machine learning models over multiple contexts
A workshop held in conjunction with the ECML PKDD 2014, Nancy, France,
15-19 September 2014
http://www.dsic.upv.es/~flip/LMCE2014/
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=== Call for Papers ===
Adaptive reuse of learnt knowledge is of critical importance in the
majority of knowledge-intensive application areas, particularly when the
context in which the learnt model operates can be expected to vary from
training to deployment. In machine learning this has been studied, for
example, in relation to variations in class and cost skew in (binary)
classification, leading to the development of tools such as ROC analysis
to adjust decision thresholds to operating conditions concerning class
and cost skew. More recently, considerable effort has been devoted to
research on transfer learning, domain adaptation, and related approaches.
Given that the main business of predictive machine learning is to
generalise from training to deployment, there is clearly scope for
developing a general notion of operating context. Without such a notion,
a model predicting sales in Prague for this week may perform poorly in
Nancy for next Wednesday. The operating context has changed in terms of
location as well as resolution. While a given predictive model may be
sufficient and highly specialised for one particular operating context,
it may not perform well in other contexts. If sufficient training data
for the new context is available it might be feasible to retrain a new
model; however, this is generally not a good use of resources, and one
would expect it to be more cost-effective to learn one general,
versatile model that effectively generalizes over multiple and possibly
previously unseen contexts.
The aim of this workshop is to bring together people working in areas
related to versatile models and model reuse over multiple contexts.
Given the advances made in recent years on specific approaches such as
transfer learning, an attempt to start developing an overarching theory
is now feasible and timely, and can be expected to generate considerable
interest from the machine learning community. Papers are solicited in
all areas relating to model reuse and model generalization including the
following areas:
* transfer learning
* data shift and concept drift
* domain adaptation
* transductive learning
* multi-task learning
* ROC analysis and cost-sensitive learning
* background knowledge
* relational learning
* context-aware applications
* incomplete information, abduction
* meta-learning
=== Submission of Papers ===
We welcome submissions describing work in progress as well as more
mature work related to learning over multiple contexts. Submissions
should be between 6 and 16 pages in the same format as the main
conference (LNAI). Authors of accepted papers will be asked to prepare a
poster, and selected authors will be given the opportunity of a plenary
presentation during the workshop.
Submission website: https://www.easychair.org/conferences/?conf=lmce2014
After the workshop, contributing authors will be invited to submit a
paper to a special issue of the Machine Learning journal dedicated to
the topic of the workshop.
=== Important Dates ===
Submission: 20 June 2014
Notification: 11 July 2014
Final verion: 25 July 2014
=== Program Committee ===
Chowdhury Farhan Ahmed, University of Strasbourg, France
Charles Elkan, University of California - San Diego, USA
Amaury Habrard, University Jean Monnet (UJM) of Saint-Etienne, France
Francisco Herrera, Universidad de Granada, Spain
Meelis Kull, University of Bristol, UK
Dragos Margineantu, Boeing Research, USA
Weike Pan, Shenzhen University, China
Joaquin Quiñonero, Facebook, USA
María José Ramírez-Quintana, Universitat Politècnica de València, Spain
Carlos Soares, University of Porto, Portugal
Masashi Sugiyama, Tokyo Institute of Technology, Japan
Bianca Zadrozny, Federal University of Fluminense, Brazil
Huimin Zhao, University of Wisconsin-Milwaukee, USA
=== Organising Committee ===
Cèsar Ferri, Technical University of Valencia, Spain (cfe...@dsic.upv.es)
Peter Flach, University of Bristol, UK (peter.fl...@bristol.ac.uk)
Nicolas Lachiche, University of Strasbourg, France
(nicolas.lachi...@unistra.fr)
For more information visit http://www.dsic.upv.es/~flip/LMCE2014/
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