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A major seaside resort town, just 1 hour away from central London.
- Abstract deadline: April 17 May 1, 2022 (extended)
- Paper deadline: April 24 May 8, 2022 (extended)
- Author notification: June 23, 2022
- Camera-ready: July 7, 2022
- Prof. Rina Barber (University of Chicago, USA)
- Prof. Eyke Hüllermeier (University of Munich, Germany)
- Dr. Sébastien Destercke (University of Technology of Compiègne, France)
Prof. Rina Foygel Barber
Department of Statistics at University of Chicago, USA
Talk details TBC
Barber is a Professor in the Department of Statistics at the University of Chicago. Before starting at U of C, she was a NSF postdoctoral fellow during 2012-13 in the Department of Statistics at Stanford University. She received her PhD in Statistics at the University of Chicago in 2012.
Her research interests are in developing and analyzing estimation, inference, and optimization tools for structured high-dimensional data problems such as sparse regression, sparse nonparametric models, and low-rank models. She works on developing methods for false discovery rate control in settings where we may have undersampled data or misspecified models, and for scalable optimization techniques for nonconvex problems.
Prof. Eyke Hüllermeier
Department of Computer Science at University of Munich, Germany
Talk details TBC
Hüllermeier is a full professor at the LMU Munich, Germany, where he is the Chair of Artificial Intelligence and Machine Learning. He graduated in mathematics and business computing, received his PhD in computer science from the University of Paderborn in 1997, and a Habilitation degree in 2002. He held professorships at the Universities of Marburg (2002-04), Dortmund (2004), Magdeburg (2005-06) and again Marburg (2007-14).
His research interests are centered around methods and theoretical foundations of artificial intelligence, with a specific focus on machine learning and reasoning under uncertainty. He has published more than 300 articles on these topics in top-tier journals and major international conferences, and several of his contributions have been recognized with scientific awards. He is a coordinator of the EUSFLAT working group on Machine Learning and Data Mining and head of the IEEE CIS Task Force on Machine Learning.
Dr. Sébastien Destercke
Heudiasyc Laboratory at University of Technology of Compiègne, France
Uncertain data in learning: challenges and opportunities
How to account for uncertain data in learning and estimation procedures is an old problem, including for example issues such as censored or missing data, the use of soft labels, etc.
In this talk, I will start by discussing the nature of data uncertainty, arguing that this uncertainty can be of non-statistical nature, and that uncertainty models generalising both probabilities and sets are interesting tools to model uncertain data in general. From there, I will start by describing some challenges arising when one has to learn in presence of uncertain data, and will finish on a more positive note by showing some settings where modelling data uncertainty can actually be beneficial to the learning procedure. I will also try to connect such modelling, challenges and opportunities to conformal or Venn-Abers predictors.
Destercke graduated from the Faculté Polytechnique de Mons as an Engineer with a specialization in computer science and applied mathematics. Since October 2011, he is a CNRS researcher in the Heuristique et Diagnostic des Systèmes Complexes research unit, and leading the CID team since September 2020.
Most of his research focuses on reasoning under severe uncertainty, where by severe it is understood incomplete and imprecise information. He has in particular investigated theories using probability sets rather than single probabilities as models of such uncertainty. His work is shared between theoretical issues and more applied considerations.
The 11th Symposium on Conformal and Probabilistic Prediction with Applications (COPA 2022) will be held from August 24th to 26th, 2022, at University of Brighton, United Kingdom. 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 method that allows to make valid predictions under relatively weak statistical assumptions. CP can be used to form set predictions, using any underlying point predictor, allowing the error levels to be controlled by the user. Therefore, CPs have been widely applied to many practical real life challenges.
Building on the work on CP, various extensions have been developed recently. 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 of the symposium include, but are not limited to:
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.
All aspects of the submission and notification process will be handled online via the EasyChair Conference System at:
https://easychair.org/conferences/?conf=copa2022
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 (physically or online) to present the work.
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).
There will be two Alexey Chervonenkis awards for the Best Paper and Best Student Paper, presented at the conference. Each awardee will receive £100 and a certificate.
Researchers interested in Conformal Prediction may be interested in joining our online discussion group. Future announcements and related materials will be published regularly.
Located in the center of Brighton, just steps from the sea, the building's extraordinary peaks and spires look is hard to miss.
Over in the pavilion's former Royal Stables and Riding School is the Brighton Museum & Art Gallery. This first-rate museum is worth visiting for its impressive collection of Art Deco pieces, its costume gallery with fashions from the 18th century.
The pier is populated with its amusement arcades, joke shops, fish-and-chip stands, and other fun things to do, including state-of-the-art thrill rides and game arcades.
Undoubtedly one of the most impressive new attractions on England's south coast, the British Airways i360 Viewing Tower is a must-visit.
The structure's circular observation platform can lift up to 200 people to heights of 453 feet for a spectacular view of the surrounding area and over the English Channel. Other features include a tearoom and gift shop.
Underneath the town's train station, the toy museum contains a vast array of vintage, rare, and unique toys from Britain and Europe.
Highlights of the museum's vast collection include antique model trains by Hornby; stuffed bears by Steiff; die-cast cars by Corgi; and all sorts of dolls, toy soldiers, farmyards, circuses, planes, and puppets.
University of Brighton
United Kingdom