13th Symposium on Conformal and Probabilistic Prediction with Applications

Milan

Theme


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 Submissions are invited on original and previously unpublished research concerning all aspects of conformal and probabilistic prediction.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


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, drug discovery, medicine, large language model, transportation, robotics 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.
  • Conformal prediction for non-euclidean data.
  • Conformal prediction for functional and high dimensional data.
  • Multi-output conformal prediction.
  • Conformal prediction for time-series or spatially dependent data.
  • Conformal decision theory.
  • Venn prediction and other methods of multi-probability prediction.
  • Distributional prediction and conformal predictive distributions.
  • Algorithmic theory of randomness.
  • Software implementations of conformal prediction frameworks and methods.
  • 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.

Paper submission


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. The LaTeX package is available here.

All aspects of the submission and notification process will be handled online via the EasyChair Conference System at:

https://easychair.org/conferences/?conf=copa20240

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.

Poster submission


Authors are invited to submit original, English-language research abstracts. The contributions should not be longer than 300 words.

Submitted abstracts will be refereed for quality, correctness, originality, and relevance. Notification and reviews will be communicated via email. All accepted contributions will be presented during the Poster Session at the Symposium.

All aspects of the submission and notification process will be handled online via the EasyChair Conference System at:

https://easychair.org/conferences/?conf=copa20240

Please note that the submission of an abstract should be regarded as a commitment that, in case the poster is accepted, at least one of the authors will register and attend the symposium to attend the poster session.

Publication


Submitted papers will be refereed for quality, correctness, originality, and relevance. Notification and reviews will be communicated via email. All accepted papers will be presented at the Symposium and published in the PMLR (Proceedings of Machine Learning Research).

Alexey Chervonenkis awards


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

Research group


Researchers interested in Conformal Prediction may be interested in joining our online discussion group. Future announcements and related materials will be published regularly.

https://groups.google.com/conformal-prediction

Instructions for the Authors of Accepted Papers


All accepted papers will be presented at the conference and published by the Proceedings of Machine Learning Research (PMLR). Volume 230 at http://proceedings.mlr.press/v230/

Please make sure to use the correct style file; it should be already installed on your computer, or will be installed "on the fly". 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[wcp]{jmlr}
									  \usepackage{amsmath,amssymb,graphicx,url}
									  \jmlrvolume{230}
									  \jmlryear{2024}
									  \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{Simone Vantini, Matteo Fontana, Aldo Solari, Henrik Boström and Lars Carlsson}

									  \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 http://jmlr.org/proceedings/faq.html (under the heading "What is the Style File for the Proceedings?"); COPA 2024 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 advices 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).

Italy Visa Information


Please notice that non-EU citizens might require a VISA to come to Italy.
To know if you need a VISA and in that case to know how to get a VISA you can refer the indication reported at this link: https://vistoperitalia.esteri.it/home/en

Organised By

Silver Sponsor

13th Symposium on Conformal and Probabilistic Prediction with Applications