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

• - Theoretical analysis of conformal prediction, including performance guarantees and optimality results.
• - Experimental studies of conformal prediction.
• - 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.

There will be three Alexey Chervonenkis awards for the "Best Paper", "Best Student Paper", and "Best Poster", presented at the conference.

Authors are invited to submit original, English-language research contributions or experience reports. Typical papers should not exceed 20 pages, and formatted according to JMLR (Journal of Machine Learning Research) template and style guidelines, available here.

The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.

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.

All accepted papers will be presented at the symposium and published in the Proceedings of Machine Learning Research (PMLR).

Authors are invited to submit an 2-page extended abstract (excluding references), formatted according to JMLR (Journal of Machine Learning Research) template and style guidelines, available here.

The Microsoft CMT service was used for managing the peer-reviewing process for this conference. This service was provided for free by Microsoft and they bore all expenses, including costs for Azure cloud services as well as for software development and support.

Accepted abstracts will be published in the Proceedings of Machine Learning Research (PMLR), and presented as a physical poster during the symposium.