The aim of this conference 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.

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. CP has been widely used to develop robust forms of probabilistic prediction methodologies, and applied to many practical real life challenges.

Topics include, but are not limited to:

- Theoretical analysis of conformal prediction, including performance guarantees and optimality results.
- Applications of conformal prediction in various fields, including bioinformatics, medicine, large language models and information security.
- Software implementations of conformal and probabilistic prediction frameworks and methods.
- Novel conformity measures.
- Distribution-free uncertainty quantification.
- Conformal anomaly detection.
- Conformal martingale testing and change-point detection.
- Venn prediction and other methods of multi-probability prediction.
- Distributional prediction and conformal predictive distributions.
- Algorithmic theory of randomness.
- Conformal prediction for explainability, causality and fairness, accountability and transparency (FAT).
- Probabilistic prediction.
- On-line compression modelling.

The committee is open to consider any other recent and cutting edge development related to Conformal and Probabilistic Prediction.

All accepted papers will be presented at the conference and published in the PMLR (Proceedings of Machine Learning Research). Good papers will be invited to submit an extended version in a journal special issue.