C A L L F O R P A P E R S

Uncertainty in Machine Learning

Workshop (combined with a tutorial) at ECML/PKDD 2020 September 14–18, 2020, Ghent, Belgium

https://sites.google.com/view/wuml-2020/

Motivation and Focus

The notion of uncertainty is of major importance in machine learning and constitutes a key element of modern machine learning methodology. In recent years, it has gained in importance due to the increasing relevance of machine learning for practical applications, many of which are coming with safety requirements. In this regard, new problems and challenges have been identified by machine learning scholars, which call for new methodological developments. Indeed, while uncertainty has long been perceived as almost synonymous with standard probability and probabilistic predictions, recent research has gone beyond traditional approaches and also leverages more general formalisms and uncertainty calculi. For example, a distinction between different sources and types of uncertainty, such as aleatoric and epistemic uncertainty, turns out to be useful in many machine learning applications. The workshop will pay specific attention to recent developments of this kind.

This workshop will be preceded by a tutorial, which provides an introduction to the topic of uncertainty in machine learning and gives an overview of existing methods and hitherto approaches to dealing with uncertainty.

Aim and Scope

The goal of this workshop is to bring together researchers interested in the topic of uncertainty in machine learning. It is meant to provide a forum for the discussion of the most recent developments in the modeling, processing, and quantification of uncertainty in machine learning problems, and the exploration of new research directions in this field. We welcome papers on all facets of uncertainty in machine learning. We solicit original work, which can be theoretical, practical, or applied, and also encourage the submission of work in progress as well as position papers or critical notes. The scope of the workshop covers, but is not limited to, the following topics:

-- adversarial examples -- belief functions -- calibration -- classification with reject option -- conformal prediction -- credal classifiers -- deep learning and neural networks -- ensemble methods -- epistemic uncertainty -- imprecise probability -- likelihood and fiducial inference -- model selection and misspecification -- multi-armed bandits -- online learning -- noisy data and outliers -- out-of-sample prediction -- performance evaluation -- hypothesis testing -- probabilistic methods -- Bayesian machine learning -- reliable prediction -- set-valued prediction -- uncertainty quantification

Submission and Review Process

Authors are supposed to submit original work in the form of regular and short papers written in English. The length of the papers is limited to 6 pages for short contributions (reporting work in progress) and 12 pages for regular contributions (reporting on more mature work) in LNCS format. All papers must be submitted in PDF format online via the EasyChair submission interface:

https://easychair.org/conferences/?conf=wuml2020

Each submission will be evaluated by at least two members of the programme committee on the basis of its relevance to the workshop, the significance and technical quality of the contribution, and the quality of presentation. All accepted papers will be included in the workshop proceedings and will be publicly available on the conference web site (unless authors opt out). Currently, possibilities for a follow-up publication are also explored, for example a special issue in a journal. At least one author of each accepted paper is required to attend the workshop to present.

Invited Speakers:

• Meelis Kull, University of Tartu, Associate Professor in Machine Learning and Chair of Data Science

More T.B.A.

Important Dates:

11 June 2020 --- paper submission 20 July 2020 --- notification of acceptance or rejection 27 July 2020 --- camera-ready version 14th or 18th September --- workshop date

Organization:

Eyke Hüllermeier, Paderborn University, eyke(a)upb.de Sébastien Destercke, Heudiasyc, Compiegne, sebastien.destercke(a)hds.utc.fr

Programme Committee:

• Viktor Bengs, Paderborn University
• Hendrik Boström, KTH Royal Institute of Technology, Sweden
• Giorgio Corani, Istituto Dalle Molle di Studi sull'Intelligenza Artificiale
• Ines Couso, University of Oviedo
• Cassio de Campos, Eindhoven University
• Juan J, Del Coz, University of Oviedo
• Thierry Denoeux, University of Technology of Compiègne
• Stefan Depeweg, Siemens
• Peter Flach, Bristol University
• Hanno Gottschalk, University of Wuppertal
• Ulf Johansson, Jönköping University
• Meelis Kull, University of Tartu
• Benjamin Quost, University of Technology of Compiègne
• Steffen Udluft, Siemens
• Vladimir Vovk, Royal Holloway, University of London
• Willem Waegeman, Ghent University
• Marco Zaffalon, Istituto Dalle Molle di Studi sull'Intelligenza Artificiale