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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/home
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Motivation and Focus
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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.
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Aim and Scope
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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
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Submission and Review Process
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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/my/conference?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.
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.
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Invited Speakers:
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Meelis Kull, Institute of Computer Science, University of Tartu
N.N.
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Important Dates:
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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
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Organization:
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Eyke Hüllermeier, Paderborn University, e...@upb.de
Sébastien Destercke, Heudiasyc, Compiegne, sebastien.dester...@hds.utc.fr
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Programme Committee:
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T.B.A.
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