- SIPTA News - lists.sipta.org

[SIPTA] SLSP 2020: call for posters
by IRDTA 15 Jun '20

15 Jun '20

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[SIPTA] IPMU'2020, IPMU"2022, and IPMU'2024 Information Processing and Management of Uncertainty in Knowledge-Based Systems
by Kreinovich 15 Jun '20

15 Jun '20

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Re: [SIPTA] Kind Reminder：Call for Abstracts for the ICOSSAR2021, June 21-25, 2021, Shanghai, China
by Kreinovich 14 Jun '20

14 Jun '20

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[SIPTA] [CFP] Extended deadline (26/6): uncertainty in machine learning workshop (WUML) at ECML/PKDD
by Sébastien Destercke 11 Jun '20

11 Jun '20

---------------------------------------------------------- C A L L F O R P A P E R S (extended deadline: 26th June) Uncertainty in Machine Learning Workshop (combined with a tutorial) at ECML/PKDD 2020 September 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. ---------------------------------------------------------- Current Invited Speakers: ---------------------------------------------------------- * Meelis Kull, University of Tartu, Associate Professor in Machine Learning and Chair of Data Science ---------------------------------------------------------- Important Dates: ---------------------------------------------------------- 26th June 2020 --- paper submission 24th July 2020 --- notification of acceptance or rejection 30th July 2020 --- camera-ready version 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

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[SIPTA] BigDat 2020 Autumn: early registration June 26
by IRDTA 09 Jun '20

09 Jun '20

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[SIPTA] IFSA/EUSFLAT'2021: mark your calendars
by Kreinovich 08 Jun '20

08 Jun '20

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[SIPTA] [Online Conference]{3rd batch CFP} -9th International Conference on Data Mining & Knowledge Management Process (CDKP 2020)
by s kareem 08 Jun '20

08 Jun '20

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[SIPTA] CfP: Workshop on “Explainable Logic-Based Knowledge Representation” (XLoKR 2020) in conjunction with KR 2020
by Francesco Kriegel 08 Jun '20

08 Jun '20

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[SIPTA] CFP Workshop on Uncertainty in Machine Learning at ECML/PKDD 2020
by Sébastien Destercke 04 Jun '20

04 Jun '20

---------------------------------------------------------- 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

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[SIPTA] TPNC 2020: 2nd call for papers
by IRDTA 04 Jun '20

04 Jun '20

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