The 13th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2024) will be held from the 9th to the 11th of September 2024, at Politecnico di Milano, Leonardo Campus, Piazza Leonardo da Vinci, Italy. Submissions are invited on original and previously unpublished research concerning all aspects of conformal and probabilistic prediction. The accepted papers will be published in the Proceedings of Machine Learning Research, Volume 230.

Conformal prediction (CP) is a modern machine and statistical learning method that allows to develop valid predictions under weak probabilistic assumptions. CP can be used to form set predictions, using any underlying point predictor, and for very general target variables, allowing the error levels to be controlled by the user. Therefore, CP has been widely used to develop robust forms of probabilistic prediction methodologies, and applied to many practical real life challenges.

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, including their application to interesting problems in any field.

Topics of the symposium include, but are not limited to:

- Theoretical analysis of conformal prediction, including performance guarantees and optimality results.
- Applications of conformal prediction.
- Novel conformity measures.
- Distribution-free uncertainty quantification.
- Conformal anomaly detection.
- Conformal martingale testing and change-point detection.
- Conformal prediction for non-euclidean data.
- Conformal prediction for functional and high dimensional data, Multi-output conformal prediction.
- Conformal prediction for temporally or spatially dependent data.
- Conformal decision theory.
- Venn prediction and other methods of multi-probability prediction.
- Distributional prediction and Conformal Predictive Distributions.
- Algorithmic information theory.
- Software implementations of conformal prediction frameworks and methods.
- Conformal prediction for Explainability, Causality and Fairness, Accountability and Transparency (FAT).