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 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. https://cmt3.research.microsoft.com/COPA2026

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), Volume 329.

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. https://cmt3.research.microsoft.com/COPA2026

Accepted abstracts will be published in the Proceedings of Machine Learning Research (PMLR), Volume 329; and presented as a physical poster in a special poster session during the symposium.

All accepted papers will be presented at the conference and published in the Proceedings of Machine Learning Research (PMLR), Volume 329.

Please make sure to use the correct style file. The camera-ready papers should follow the style of the Proceedings section of JMLR rather than JMLR itself.

In the end you should prepare two files: (1) your paper in PDF format; (2) the copyright form (http://proceedings.mlr.press/pmlr-license-agreement.pdf). Please upload the final version of your paper and the signed copyright form on EasyChair.

The beginning of your file will look like:

									  \documentclass[pmlr]{jmlr}
									  \usepackage{amsmath,amssymb,graphicx,url}
									  \jmlrvolume{329}
									  \jmlryear{2026}
									  \jmlrworkshop{Conformal and Probabilistic Prediction with Applications}
									  \jmlrproceedings{PMLR}{Proceedings of Machine Learning Research}

									  \title{Nonparametric predictive distributions based on conformal prediction}
									  \author{\Name{John Smith}\Email{j.smith@gmail.com}\\
										\addr{Royal Holloway, University of London, Egham, Surrey, UK}\\
										\Name{Minge Shen}\Email{m.shen@gmail.com}\\
										\addr{Rutgers University, New Brunswick, NJ, USA}}
										
									  \editor{Ernst Ahlberg, Einav Peretz Andersson, Johan Hallberg Szabadváry}

									  \begin{abstract}
										This paper applies conformal prediction to derive predictive distributions that are valid under a nonparametric assumption.
									  \end{abstract}

									  \begin{keywords}
										Conformal prediction, predictive distributions, regression.
									  \end{keywords}
									

Notice the presence of the command

									  \jmlrproceedings{PMLR}{Proceedings of Machine Learning Research}
									

Please let us know if you encounter any problems following these instructions.

For further information (which, however, can be confusing), see https://proceedings.mlr.press/faq.html (under the heading "What is the Style File for the Proceedings?"); COPA 2026 uses the one column style file. The style file is available from the CTAN (Comprehensive TeX Archive Network) web site at http://ctan.org/tex-archive/macros/latex/contrib/jmlr you can go inside the directory called "sample_papers" and emulate the files jmlr_sample.tex and jmlr_sample.bib (the latter is only needed if you use bibtex).

Some useful advice on the JMLR style can be found at http://jmlr.org/format/format.html (However, please make sure to use the Proceedings style, as described above, rather than the main journal style).