CoPA 2013 - 2nd Workshop on Conformal Prediction and its Applications

Quantifying the uncertainty of the predictions produced by classification and regression techniques is an important problem in the field of Machine Learning. Conformal Prediction is a recently developed framework for complementing the predictions of Machine Learning algorithms with reliable measures of confidence. The methods developed based on this framework produce well-calibrated confidence measures for individual examples without assuming anything more than that the data are generated independently by the same probability distribution (i.i.d.).
Since its development the framework has been combined with many popular techniques, such as Support Vector Machines, k-Nearest Neighbours, Neural Networks, Ridge Regression etc., and has been successfully applied to many challenging real world problems, such as the early detection of ovarian cancer, the classification of leukaemia subtypes, the diagnosis of acute abdominal pain, the assessment of stroke risk, the recognition of hypoxia in electroencephalograms (EEGs), the prediction of plant promoters, the prediction of network traffic demand, the estimation of effort for software projects and the backcalculation of non-linear pavement layer moduli. The framework has also been extended to additional problem settings such as semi-supervised learning, anomaly detection, feature selection, outlier detection, change detection in streams and active learning.
The aim of this workshop is to serve as a forum for the presentation of new and ongoing work and the exchange of ideas between researchers on any aspect of Conformal Prediction and its applications. The workshop welcomes submissions introducing further developments and extensions of the Conformal Prediction framework and describing its application to interesting problems of any field.
Topics of interest
Topics of interest include, but are not limited to:
Non-conformity measures
Modifications of the framework
Venn prediction
On-line compression modeling
Extensions to additional problem settings
Theoretical analysis of Conformal Prediction techniques
Applications/usages of Conformal Prediction
Honorary Chairs
Vladimir Vapnik, NEC, USA and Royal Holloway, University of London, UK
Alexei Chervonenkis, Russian Academy of Sciences, Russia and Royal Holloway, University of London, UK
Workshop Chairs
Harris Papadopoulos, Frederick University, Cyprus h.papadopoulos-AT-frederick.ac.cy
Alex Gammerman, Royal Holloway, University of London, UK alex-AT-cs.rhul.ac.uk
Vladimir Vovk, Royal Holloway, University of London, UK vovk-AT-cs.rhul.ac.uk
Academic Program Committee
Vineeth Balasubramanian, Arizona State University, USA
Anthony Bellotti, Imperial College London, UK
Martin Eklund, Uppsala University, Sweden
David R. Hardoon, SAS Singapore
Mohamed Hebiri, Université de Marne-la-Vallée, France
Shen-Shyang Ho, Nanyang Technological University, Singapore
Yuri Kalnishkan, Royal Holloway University of London, UK
Matjaz Kukar, University of Ljubljana, Slovenia
Antonis Lambrou, Royal Holloway University of London, UK
Rikard Laxhammar, University of Skovde, Sweden
Jing Lei, Carnegie Mellon University, USA
Yang Li, Chinese Academy of Sciences, China
Zhiyuan Luo, Royal Holloway University of London, UK
Andrea Murari, Consorzio RFX, Italy
Ilia Nouretdinov, Royal Holloway University of London, UK
Klea Panayidou, Frederick University, Cyprus
Savvas Pericleous, Frederick University, Cyprus
Frank-Michael Schleif, Bielefeld University, Germany
David Surkov, Egham Capital, UK
Jesus Vega, Asociación EURATOM/CIEMAT para Fusión, Spain
Larry Wasserman, Carnegie Mellon University, USA
Fan Yang, Xiamen University, China