20-22 April, 2016

CIEMAT, Madrid, Spain

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

Symposium Theme

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

Prof. V. Vapnik Prof. Vladimir 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.


The topics of the symposium include, but are not limited to:


Authors are invited to submit original, English-language research contributions or experience reports. Papers should be no longer than 20 pages formatted according to the well-known LNCS Springer style. All aspects of the submission and notification process will be handled online via the EasyChair Conference System.


Submitted papers will be refereed for quality, correctness, originality, and relevance. Notification and reviews will be communicated via email. 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 the 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.

Important Dates


Yandex Logo