10th Symposium on Conformal and Probabilistic Prediction with Applications

Theme


The main purpose of conformal prediction is to complement predictions delivered by various algorithms of Machine Learning with provably valid measures of their accuracy and reliability under the assumption that the observations are independent and identically distributed. It was originally developed in the late 1990s and early 2000s but has become more popular and further developed in important directions in recent years.

Conformal prediction is a universal tool in several senses; in particular, it can be used in combination with any known machine-learning algorithm, such as SVM, Neural Networks, Ridge Regression, etc. It has been applied to a variety of problems from diagnostics of depression to drug discovery to the behaviour of bots.

A sister method of Venn prediction was developed at the same time as conformal prediction and is used for probabilistic prediction in the context of classification. Among recent developments are adaptations of conformal and Venn predictors to probabilistic prediction in the context of regression.

The COPA series of workshops is a home for work in both conformal and Venn prediction, as reflected in its full name “Conformal and Probabilistic Prediction with Applications”. 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 and their applications 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
  • Applications of conformal prediction in various fields, including bioinformatics, drug discovery, medicine, and information security
  • Novel conformity measures
  • Conformal anomaly detection
  • Venn prediction and other methods of multiprobability prediction
  • Conformal predictive distributions
  • Probabilistic prediction
  • On-line compression modelling
  • Prediction in: Machine learning, Pattern recognition, Data mining, Transfer learning
  • Algorithmic information theory
  • Data visualization
  • Big data applications

Paper submission


Authors are invited to submit original, English-language research contributions or experience reports. Papers should be no longer than 20 pages formatted according to the well-known JMLR (Journal of Machine Learning Research) style. The LaTeX package for the style is available here.

Work-in-progress and extended abstract (up to 2 pages) are welcome for poster submission, and will be included in the conference's proceeding. The submission should be formatted according to the well-known JMLR (Journal of Machine Learning Research) style.

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

https://easychair.org/conferences/?conf=copa20210

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 152.

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 submit two files: (1) file containing your paper in the pdf format; (2) copyright form (http://proceedings.mlr.press/pmlr-license-agreement.pdf). Please upload the final version of your paper in pdf via EasyChair and email the signed copyright form to zhiyuan.luo@rhul.ac.uk

The beginning of your file will look like:

									  \documentclass[wcp]{jmlr}
									  \usepackage{amsmath,amssymb,graphicx,url}
									  \jmlrvolume{152}
									  \jmlryear{2021}
									  \jmlrworkshop{Conformal and Probabilistic Prediction and 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{Lars Carlsson, Zhiyuan Luo, Giovanni Cherubin and Khuong An Nguyen}

									  \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 2021 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).

A lot of 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).

To ensure the reproducibility of your results, and to help our community to flourish further, we encourage (but not require) that you share the code and data used for your experiments (if applicable). As a guideline, you may refer to the following link: https://github.com/paperswithcode/releasing-research-code.

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10th Symposium on Conformal and Probabilistic Prediction with Applications