SUM 2019: the 13th international conference on Scalable Uncertainty
Management
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The thirteenth international conference on Scalable Uncertainty
Management (SUM 2019) will be held in Compiègne (France), on December
16-18, 2019.
The SUM conferences are annual events which gather researchers
interested in dealing with imperfect information, in a wide range of
fields such as artificial intelligence, databases, information
retrieval, machine learning, and risk analysis, with the aim of
fostering collaboration and cross-fertilization of ideas from different
communities.
An originality of the SUM conferences is the care for dedicating a large
part of their programs to tutorials covering a wide range of topics
related to uncertainty management. Each tutorial provides a 45-minute
survey of one of the research areas in the scope of the conference.
Topics of interest:
The scope of the conference covers a wide range of topics related to the
management of large amounts of information, in particular information of
a complex kind, uncertain, incomplete, or inconsistent. We are
particularly interested in papers that focus on bridging gaps, for
instance between different communities, between numerical and symbolic
approaches, or between theory and practice. Topics of interest include
(but are not limited to):
* Imperfect information in artificial intelligence *
- Statistical relational learning, graphical models, probabilistic inference
- Argumentation, defeasible reasoning, belief revision
- Weighted logics for managing uncertainty
- Reasoning with imprecise probability, Dempster-Shafer theory,
possibility theory
- Approximate reasoning, similarity-based reasoning, analogical reasoning
- Planning under uncertainty, reasoning about actions, spatial and
temporal reasoning
- Incomplete preference specifications
- Imperfect information in databases
* Methods for modeling, indexing, and querying uncertain databases *
- Top-k queries, skyline query processing, and ranking
- Approximate, fuzzy query processing
- Uncertainty in data integration and exchange
- Uncertainty and imprecision in geographic information systems
- Probabilistic databases and possibilistic databases
- Data provenance and trust
- Data summarization
- Imperfect information in information retrieval and semantic web
applications
* Approximate schema and ontology matching *
- Uncertainty in description logics and logic programming
- Learning to rank, personalization, and user preferences
- Probabilistic language models
- Combining vector-space models with symbolic representations
- Inductive reasoning for the semantic web
* Risk analysis *
- Aleatory vs. epistemic uncertainty
- Uncertainty elicitation methods
- Uncertainty propagation methods
- Decision analysis methods
- Tools for synthesizing results
Invited keynotes:
-Cassio P. de Campos (Utrecht University, Utrecht, the Netherlands):
"Scalable reliable machine learning using sum-product networks"
- Jérôme Lang (Université Paris Dauphine, Paris, France): "Computational
social choice (provisional title)"
- Wolfgang Gatterbauer (Northeastern University, Boston, MA, USA):
"Algebraic approximations of the Probability of Boolean Functions
(provisional title)"
Important dates:
- June 30th, 2019: submission deadline
- Sept. 8th, 2019: notification of acceptance
- Sept. 22nd, 2019: camera-ready copies
- Dec. 16-18, 2019: conference
Submission guidelines and proceedings:
SUM 2019 solicits papers in the following three categories:
- Long papers: technical papers reporting original research or survey papers
- Short papers: papers reporting promising work-in-progress, system
descriptions, position papers on controversial issues, or survey papers
providing a synthesis of current research trends
- Extended abstracts of recently published work in a relevant journal or
top-tier conference
Regular research papers should be at most 14 pages (including
references, figures, and tables). Short papers should be between 4 and 7
pages. Extended abstracts should be at most 2 pages and must reference
the originally published work.
Accepted long and short papers will be published by Springer in the
Lecture Notes in Artificial Intelligence (LNAI) series. Authors of an
accepted long or short paper will be expected to sign copyright release
forms, and one author is expected to give a presentation at the
conference. Authors of accepted abstracts will be expected to present
their work during the conference, but the extended abstracts will not be
published in the LNCS/LNAI proceedings (they will be made available in a
separate booklet).
All submissions will be processed using EasyChair:
https://easychair.org/conferences/?conf=sum2019
Submissions must be formatted according to Springer's guidelines for
LNCS authors, which can be found at
http://www.springer.de/comp/lncs/authors.html. Papers not respecting the
formatting instructions or page limits may be rejected without review.
Except for extended abstracts, submissions must be unpublished and must
not be under submission elsewhere. All submitted papers will be reviewed
by the Program Committee on the basis of technical quality, relevance,
significance, and clarity.
Organization committee:
Yonatan Carlos Carranza Alarcon, Université de Technologie de Compiègne,
France
Sébastien Destercke, CNRS, Université de Technologie de Compiègne, France
Marie-Hélène Masson, Université de Picardie Jules-Verne, France
Benjamin Quost, Université de Technologie de Compiègne, France
(conference chair)
David Savourey, Université de Technologie de Compiègne, France
Program committee chairs:
Nahla Ben Amor, Institut Supérieur de Gestion de Tunis, Tunisia
Martin Theobald, University of Luxembourg, Luxembourg
Steering committee:
Didier Dubois, IRIT-CNRS, France
Lluis Godo, IIIA-CSIC, Spain
Eyke Hüllermeier, Universität Paderborn, Germany
Anthony Hunter, University College London, UK
Henri Prade, IRIT-CNRS, France
Steven Schockaert, Cardiff University, UK
V. S. Subrahmanian, University of Maryland, USA
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