• Call for Extended Abstracts: 12th Symposium on Conformal and Probabilis

    From Harris Papadopoulos@21:1/5 to All on Wed Apr 19 05:48:59 2023
    [ Please distribute - apologies for multiple postings ]

    The paper submission deadline has passed, but you can still give a short presentation of your work at COPA 2023 by submitting a 2-page extended
    abstract that will be published in the Symposium proceedings.

    The deadline for extended abstract submission is the 8th of May. You can
    submit at any time before that, though, and we will provide you with a
    decision within one week.

    *** Call for Extended Abstracts ***

    The 12th Symposium on Conformal and Probabilistic Prediction with
    Applications (COPA 2023)

    September 13-15, 2023, Miramare Beach Hotel, Limassol, Cyprus



    The 12th Symposium on Conformal and Probabilistic Prediction with
    Applications (COPA 2023) will be held from the 13th to the 15th of
    September 2023, at the Miramare Beach Hotel in Limassol, Cyprus.
    Submissions are invited on original and previously unpublished research concerning all aspects of conformal and probabilistic prediction. The
    symposium proceedings will be published in the Proceedings of Machine
    Learning Research.

    Conformal prediction (CP) is a modern machine learning framework that
    allows making valid predictions under relatively weak statistical
    assumptions. CP can be combined with any conventional predictor for
    producing set predictions with a guaranteed accuracy, thus allowing the
    error levels to be controlled by the user. Consequently, CP has been
    widely applied to practical real-life challenges and formed the basis
    for the development of many novel approaches and extensions.

    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.


    There will be two Alexey Chervonenkis awards for the best paper and the
    best student paper. Each awardee will receive a certificate and a
    monetary prize of €100.


    The topics of the symposium include, but are not limited to:

    - Theoretical analysis of conformal prediction, including
    performance guarantees
    - Novel conformity measures
    - Conformal change-point detection
    - Conformal anomaly detection
    - Conformal martingale testing
    - Conformal multi-label classification and multi-target regression
    - Venn prediction and other methods of multiprobability prediction
    - Conformal predictive distributions
    - Probabilistic prediction
    - On-line compression modelling
    - Algorithmic information theory
    - Adoption of conformal prediction to new settings
    - Implementations of conformal prediction frameworks and algorithms
    - Conformal prediction for explainable machine learning and
    Fairness, Accountability and Transparency (FAT)
    - Applications of conformal prediction in various fields, including
    bioinformatics, drug discovery, biomedicine, natural language
    processing, transportation, robotics and information security


    - Extended Abstract (2-pages) Submission Deadline: May 8th, 2023
    (decision within one week of submission)
    - Camera-ready Submission Deadline: May 31st, 2023
    - Symposium Dates: September 13th - 15th, 2023


    Extended abstracts should be no longer than 2 pages formatted according
    to the well-known JMLR (Journal of Machine Learning Research) style. The
    LaTeX package for the style is available at:


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


    Submission of an extended abstract should be regarded as a commitment
    that, should it be accepted, at least one of the authors will register
    and attend the symposium to present the work.


    All accepted extended abstracts will be presented at the Symposium and published in the PMLR (Proceedings of Machine Learning Research).

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