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*************************Call for Papers***********************************************

The 7th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2018)
June 11-13, 2018
Maastricht, The Netherlands

http://clrc.rhul.ac.uk/copa2018/

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THEME

Conformal prediction was developed originally at the end of the 1990s and summarized in the monograph "Algorithmic Learning in a Random World", Springer, New York, 2005. The main purpose of this method is to complement predictions delivered by various algorithms of Machine Learning with provably valid measures of their accuracy and reliability under the assumption that the observations are independent and identically distributed.

Conformal prediction is a universal tool in several senses; in particular, it can be used in combination with any known machine learning algorithm, such as SVM, Neural Networks, Ridge Regression, etc. It has been applied to a variety of problems from diagnostics of depression to the behaviour of bots.

A sister method of Venn prediction was developed at the same time as conformal prediction and is used for probabilistic prediction in the context of classification. Conformal prediction itself has been extended to cover probabilistic prediction in the context of regression. The COPA series of workshops/symposia is a home for work in both conformal and probabilistic prediction, as reflected in its full name "Conformal and Probabilistic Prediction with Applications". 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 and Probabilistic Prediction and their applications to interesting problems in any field.


TOPICS

Topics of the symposium include, but are not limited to:
- Theoretical analysis of conformal prediction, including performance guarantees
- Applications of conformal prediction in various fields, including bioinformatics, medicine, and information security
- Novel conformity measures
- Conformal anomaly detection
- Venn prediction and other methods of multiprobability prediction
- Conformal predictive distributions
- Probabilistic prediction
- On-line compression modelling
- Prediction in: machine learning, pattern recognition, data mining, and transfer learning
- Algorithmic information theory
- Data visualization
- Big data applications


SPECIAL SESSIONS

- Conformal Prediction in Medical Applications
- Cheminformatics


PAPER SUBMISSION

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 JMLR (Journal of Machine Learning Research) style. All aspects of the submission and notification process will be handled online via the EasyChair Conference System: https://easychair.org/conferences/?conf=copa2018

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


PUBLICATION

All accepted papers will be presented at the conference and published in the PMLR (Proceedings of Machine Learning Research).


POSTER SUBMISSION

Authors are invited to submit abstracts for the poster sessions. The poster abstract submission deadline is 4th May, 2018 and the submission will be handled online via the EasyChair Conference System. Only registered delegates can present the poster at the conference. The poster abstracts will be published on the conference website.


IMPORTANT DATES

- Paper Submission Deadline: March 19th, 2018
- Poster Abstract Submission Deadline: May 4th, 2018
- Author Notifications: April 30th, 2018
- Camera-ready Submission Deadline: May 14th, 2018
- Symposium Dates: June 11th-13th, 2018


General Chairs

- Evgueni Smirnov (The Netherlands)
- Ralf Peeters (The Netherlands)


Local Organising Committee

- Mena Habib (The Netherlands)


Programme Committee Chair

- Zhiyuan Luo (UK)


Special Sessions Chairs:

- Lars Carlsson (Sweden)
- Harris Papadopoulos (Cyprus)


Honorary Chairs

- Alex Gammerman (UK)
- Vladimir Vapnik (AI Research Facebook, Columbia University USA and Royal Holloway, University of London, UK)
- Volodya Vovk (UK)