Submission

Contact Us

Royal Holloway University of London, Egham, UK
10 - 12 September 2025

15th Symposium on Conformal and Probabilistic Prediction with Applications

Starting in:

Call for Papers

Theme

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, 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.
The Committee is open to consider any other recent and cutting edge development related to Conformal and Probabilistic Prediction.
Alexey Chervonenkis awards

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

Deadlines

- Paper submission deadline: May 1st 2026.
- Poster submission deadline: July 1st 2026.
- Author notification: June 15th 2026.
- Camera ready: July 7th 2026.
Paper publication

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).
Poster publication

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); and presented as a physical poster in a special poster session during the symposium.

Keynote Speakers

Prof Martin Eklund

Karolinska Institute

Martin Eklund is professor of epidemiology at the Department of Medical Epidemiology and Biostatistics (MEB) at Karolinska Institutet (KI) in Sweden. The overarching aim of Martin Eklund’s research is to develop personalized, high-precision cancer management systems to reduce prostate- and breast cancer mortality as well as the negative side effects of today's imprecise diagnostics and treatment selection. To achieve this, he has constructed a research program spanning biomarker validation and implementation, prediction modeling and artificial intelligence, translational medicine, and clinical trials. Eklund is an investigator of several large-scale observational studies and clinical trials to improve diagnostics and treatment of both prostate- and breast cancer. For prostate cancer, he is one of the originators of the Stockhom3 diagnostic test, and he has been a driving force in showing the benefit of MRI and risk prediction for prostate cancer screening. Eklund is also on the forefront in the development of artificial intelligence for prostate cancer pathology.

Talk details TBA

Prof Ola Engkvist

AstraZeneca & Chalmers University of Technology

Ola Engkvist, PhD, is Executive Director and Head of Molecular AI within Discovery Sciences at AstraZeneca R&D, where he leads the development and application of machine learning and artificial intelligence to accelerate drug design. He has published over 180 peer-reviewed scientific articles and is an ELLIS fellow at the European Laboratory for Learning and Intelligent Systems and a 2025 Clarivate Highly Cited Researcher. He holds an adjunct professorship in machine learning and AI for drug design at Chalmers University of Technology, serves as a Trustee of the Cambridge Crystallographic Data Centre, and is recognized for his work in pharmaceutical innovation.

Talk details TBA

Venue

Conference
Hotels
Excursion
Visa

Mölnlycke Health Care, Entreprenörsstråket 21, 431 53 Mölndal, Gothenburg, Sweden

The conference will take place at Mölnlycke Health Care’s new global headquarters in Mölndal – a modern life science district just south of central Gothenburg, known for its contemporary Scandinavian architecture and collaborative innovation spaces.

Located at Entreprenörsstråket 21, the campus is easily reached from Gothenburg in around 10–15 minutes by train or tram via Mölndal station, with frequent Västtrafik services from Göteborg central station.

The nearest airport is Göteborg Landvetter. Upon arrival, the simplest option is to take a taxi or ride-share directly to the venue (around 20 minutes), or use the Flygbussarna airport coach to Mölndals bro, then continue by local tram, bus or a short taxi ride.