Co-organised by: Royal Holloway, University of London (UK) and CIEMAT (Spain)


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 symposium 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.

Alexey Chervonenkis Memorial Lecture will be given by Prof. Vladimir Vapnik

Prof. V. Vapnik  

The symposium 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 include, but are not limited to:

  • Non-conformity measures
  • Venn prediction
  • On-line compression modeling
  • Theoretical analysis of Conformal Prediction techniques
  • Applications/usages of Conformal Prediction
  • Machine learning
  • Pattern recognition
  • Regression estimation
  • Density estimation
  • Algorithmic information theory
  • Measures of confidence
  • Applications in Bioinformatics and Medicine
  • Applications in Information Security and Homeland Security
  • Data mining and visualization
  • Big data applications
  • Data analysis applications in science and engineering

Important Dates

  • Paper Submission Deadline: January 22nd, 2016 February 5th, 2016
  • Author Notifications: February 15th,2016 February 29th, 2016
  • Camera-ready Submission Deadline: February 23rd,2016  March 8th, 2016
  • Symposium Dates: April 20-22, 2016


All accepted papers will be presented at the conference and published by Springer in a volume of Lecture Notes in Artificial Intelligence (LNAI) series. Last but not least, it should be noted that selected papers from the Symposium will be published in ‘Annals of Mathematics and Artificial Intelligence’ (AMAI) journal. The journal submissions must be substantially expanded from any LNAI versions and will undergo a separate journal refereeing procedure.