2 Search Results for "Sanner, Scott"

Document
DOI: 10.4230/DagRep.13.6.1
Scalable Analysis of Probabilistic Models and Programs (Dagstuhl Seminar 23241)

Authors: Sebastian Junges, Joost-Pieter Katoen, Scott Sanner, Guy Van den Broeck, and Bahare Salmani

Published in: Dagstuhl Reports, Volume 13, Issue 6 (2024)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 23241 "Scalable Analysis of Probabilistic Models and Programs". The seminar brought together researchers from probabilistic graphical models, verification of probabilistic programming languages, and probabilistic planning. The communities bring vastly different perspectives on the methods and goals of inference under uncertainty. In this seminar, we worked towards a common understanding of how the different angles yield subtle differences in the problem statements and how the different methods provide different strengths and weaknesses. The report describes the different areas, the activities during the seminar including hot topics that were vividly discussed, and an overview of the technical talks.
Cite as

Sebastian Junges, Joost-Pieter Katoen, Scott Sanner, Guy Van den Broeck, and Bahare Salmani. Scalable Analysis of Probabilistic Models and Programs (Dagstuhl Seminar 23241). In Dagstuhl Reports, Volume 13, Issue 6, pp. 1-21, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)

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@Article{junges_et_al:DagRep.13.6.1,
  author =	{Junges, Sebastian and Katoen, Joost-Pieter and Sanner, Scott and Van den Broeck, Guy and Salmani, Bahare},
  title =	{{Scalable Analysis of Probabilistic Models and Programs (Dagstuhl Seminar 23241)}},
  pages =	{1--21},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2024},
  volume =	{13},
  number =	{6},
  editor =	{Junges, Sebastian and Katoen, Joost-Pieter and Sanner, Scott and Van den Broeck, Guy and Salmani, Bahare},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.13.6.1},
  URN =		{urn:nbn:de:0030-drops-196362},
  doi =		{10.4230/DagRep.13.6.1},
  annote =	{Keywords: model counting, probabilistic inference, probabilistic model checking, probabilistic planning, probabilistic programs}
}
Document
DOI: 10.4230/DagRep.4.3.1
Preference Learning (Dagstuhl Seminar 14101)

Authors: Johannes Fürnkranz, Eyke Hüllermeier, Cynthia Rudin, Roman Slowinski, and Scott Sanner

Published in: Dagstuhl Reports, Volume 4, Issue 3 (2014)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 14101 "Preference Learning". Preferences have recently received considerable attention in disciplines such as machine learning, knowledge discovery, information retrieval, statistics, social choice theory, multiple criteria decision making, decision under risk and uncertainty, operations research, and others. The goal of this seminar was to showcase recent progress in these different areas with the goal of working towards a common basis of understanding, which should help to facilitate future synergies.
Cite as

Johannes Fürnkranz, Eyke Hüllermeier, Cynthia Rudin, Roman Slowinski, and Scott Sanner. Preference Learning (Dagstuhl Seminar 14101). In Dagstuhl Reports, Volume 4, Issue 3, pp. 1-27, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2014)

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@Article{furnkranz_et_al:DagRep.4.3.1,
  author =	{F\"{u}rnkranz, Johannes and H\"{u}llermeier, Eyke and Rudin, Cynthia and Slowinski, Roman and Sanner, Scott},
  title =	{{Preference Learning (Dagstuhl Seminar 14101)}},
  pages =	{1--27},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2014},
  volume =	{4},
  number =	{3},
  editor =	{F\"{u}rnkranz, Johannes and H\"{u}llermeier, Eyke and Rudin, Cynthia and Slowinski, Roman and Sanner, Scott},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.4.3.1},
  URN =		{urn:nbn:de:0030-drops-45506},
  doi =		{10.4230/DagRep.4.3.1},
  annote =	{Keywords: machine learning, preference learning, preference elicitation, ranking, social choice, multiple criteria decision making, decision under risk and unce information retrieval}
}
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