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. The COPA series of workshops/symposia 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 of any field..
- 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, Transfer learning
- Algorithmic information theory
- Data visualization
- Big data applications
- Conformal Prediction in Medical Applications
- Drug Discovery
Frequently asked questions
- Paper Submission Deadline (extended):
March 19th, 2018March 29th, 2018
- Author Notifications: April 30th, 2018
- Poster Abstract Submission Deadline: May 4th, 2018
- Camera-ready Submission Deadline: May 14th, 2018
- Symposium Dates: June 11th-13th, 2018
All accepted papers will be presented at the conference and published by PMLR (Proceedings of Machine Learning Research).
Only registered delegates can present the poster at the conference. Unfortunately, we can only include full papers in the conference proceedings. However, we plan to place all the poster abstracts on the conference website.