September 27th, 2013 - Workshop: Tensor Methods for Machine Learning

ECML/PKDD Workshop: Tensor Methods for Machine Learning
Date: Friday, September 27th, 2013
Location: Prague, Czech Republic
Website: http://www.cip.ifi.lmu.de/~nickel/tml2013/
Call for Papers
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Tensors, as generalizations of vectors and matrices, have become increasingly popular in different areas of machine learning and data mining, where they are employed to approach a diverse number of difficult learning and analysis tasks. Prominent examples include learning on multi-relational data and large-scale knowledge bases, recommendation systems, computer vision, mining boolean data, signal processing, neuroimaging or the analysis of time-varying networks. The success of tensors methods is strongly related to their ability to efficiently model, analyze and predict data with multiple modalities. To address specific challenges and problems, a variety of methods has been developed in different fields of application. This workshop should serve as a basis for an interdisciplinary exchange of methods, ideas and techniques, with the goal to develop a deeper understanding of tensor methods for machine learning, further advance existing approaches and enable new approaches to important problems. A particular focus of this workshop is to uncover underlying principles in tensor methods, their applications, and associated problems. The workshop is intended for researchers in the machine learning, data mining, and tensor communities to discuss novel methods and applications as well as theoretical advances.
Topics of interest include, but are not limited to
? Statistical analysis and learning theory related to tensor methods, factorizations or analysis.
? Novel techniques and methods for tensor factorization or tensor completion.
? New factorization models, loss functions or regularization methods.
? Probabilistic/Bayesian approaches to tensor factorizations.
? Tensor methods for large-scale and distributed problems.
? Novel applications of tensor methods in machine learning and statistics.
? Empirical studies that provide new insight into tensor methods for machine learning.
? Related techniques and methods in machine learning such as matrix factorizations.
More information about the workshop is available from the workshop's website at:
http://www.cip.ifi.lmu.de/~nickel/tml2013/
Submission Instructions
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Submitted papers should be at most 4 (extended abstract) or 8 (full paper) pages long and formatted according to the Springer LNAI guidelines.
All submitted papers will be subject to peer review. Accepted papers will be presented as a talk or poster at the workshop and will also be published on the workshop's website.
For manuscript submission, please use the EasyChair site at:
https://www.easychair.org/conferences/?conf=tml201...
Important Dates
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For manuscript submission, please consider the following deadlines
? Paper Submission: Friday, June 28th, 2013
? Acceptance Notification: Friday, July 19th, 2013
? Camera-Ready Paper Submission: Friday, August 2nd, 2013
Invited Speakers
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To be announced
Program Chairs
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? Maximilian Nickel, Ludwig Maximilian University Munich, Germany
? Volker Tresp, Siemens AG, Germany
Program Committee (confirmed to date)
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? Alwin Stegeman, University of Groningen, Netherlands
? Evrim Acar Ataman, University of Copenhagen, Denmark
? Franz Király, TU Berlin, Germany
? Jaakko Hollmén, Aalto University, Finland
? Morten Mørup, Technical University of Denmark, Denmark
? Pauli Miettinen, Max-Planck Institut für Informatik, Germany
? Rainer Gemulla, Max-Planck Institut für Informatik, Germany
? Ryota Tomioka, University of Tokyo, Japan
? Shipeng Yu, Siemens Medical Solutions USA, USA
? Steffen Rendle, Universität Konstanz, Germany
? Taylan Cemgil, Bogazici University Istanbul, Turkey