Theme

The main purpose of conformal prediction 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. It was originally developed in the late 1990s and early 2000s but has become more popular and further developed in important directions in recent years.

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 drug discovery 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. Among recent developments are adaptations of conformal and Venn predictors to probabilistic prediction in the context of regression.
The COPA series of workshops is a home for work in both conformal and Venn 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, drug discovery, 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, Transfer learning
* Algorithmic information theory
* Data visualization
* Big data applications

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. 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=copa2019