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COPA 2023 : 12th Symposium on Conformal and Probabilistic Prediction with Applications | |||||||||||||||||
Link: https://copa-conference.com/ | |||||||||||||||||
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Call For Papers | |||||||||||||||||
The 12th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2023) will be held from September 13th to 15th, 2023, in Limassol, Cyprus. Submissions are invited on original and previously unpublished research concerning all aspects of conformal and probabilistic prediction. The symposium proceedings will be published in the Proceedings of Machine Learning Research.
Conformal prediction (CP) is a modern machine learning method that allows to make valid predictions under relatively weak statistical assumptions. CP can be used to form set predictions, using any underlying point predictor, allowing the error levels to be controlled by the user. Therefore, CPs have been widely applied to many practical real life challenges. Building on the work on CP, various extensions have been developed recently. 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 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, drug discovery, medicine, and information security Novel conformity measures Conformal change-point detection Conformal anomaly detection Conformal martingale testing 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, Transfer learning Algorithmic information theory Implementations of conformal prediction frameworks and algorithms Conformal prediction for explainable machine learning and Fairness, Accountability and Transparency (FAT) Data visualization Big data applications 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. The LaTeX package for the style is available here. All aspects of the submission and notification process will be handled online via the EasyChair Conference System at: https://easychair.org/conferences/?conf=copa2023 Submission of a paper should be regarded as a commitment that, should the paper be accepted, at least one of the authors will register and attend the symposium to present the work. 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 Symposium and published in the PMLR (Proceedings of Machine Learning Research), Volume 204. |
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