3 Search Results for "H�llermeier, Eyke"

Document
DOI: 10.4230/DagRep.10.4.1
Beyond Adaptation: Understanding Distributional Changes (Dagstuhl Seminar 20372)

Authors: Georg Krempl, Vera Hofer, Geoffrey Webb, and Eyke Hüllermeier

Published in: Dagstuhl Reports, Volume 10, Issue 4 (2021)

Abstract
This report documents the program and the outcomes of Dagstuhl Seminar 20372 "Beyond Adaptation: Understanding Distributional Changes". It was centered around the aim to establish a better understanding of the causes, nature and consequences of distributional changes. Four key research questions were identified and discussed in during the seminar. These were the practical relevance of different scenarios and types of change, the modelling of change, the detection and measuring of change, and the adaptation to change. The seminar brought together participants from several distinct communities in which parts of these questions are already studied, albeit in separate lines of research. These included data stream mining, where the focus is on concept drift detection and adaptation, transfer learning and domain adaptation in machine learning and algorithmic learning theory, change point detection in statistics, and the evolving and adaptive systems community. Therefore, this seminar contributed to stimulate research towards a thorough understanding of distributional changes.
Cite as

Georg Krempl, Vera Hofer, Geoffrey Webb, and Eyke Hüllermeier. Beyond Adaptation: Understanding Distributional Changes (Dagstuhl Seminar 20372). In Dagstuhl Reports, Volume 10, Issue 4, pp. 1-36, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)

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@Article{krempl_et_al:DagRep.10.4.1,
  author =	{Krempl, Georg and Hofer, Vera and Webb, Geoffrey and H\"{u}llermeier, Eyke},
  title =	{{Beyond Adaptation: Understanding Distributional Changes (Dagstuhl Seminar 20372)}},
  pages =	{1--36},
  journal =	{Dagstuhl Reports},
  ISSN =	{2192-5283},
  year =	{2021},
  volume =	{10},
  number =	{4},
  editor =	{Krempl, Georg and Hofer, Vera and Webb, Geoffrey and H\"{u}llermeier, Eyke},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagRep.10.4.1},
  URN =		{urn:nbn:de:0030-drops-137359},
  doi =		{10.4230/DagRep.10.4.1},
  annote =	{Keywords: Statistical Machine Learning, Data Streams, Concept Drift, Non-Stationary Non-IID Data, Change Mining, Dagstuhl Seminar}
}
Document
DOI: 10.4230/DagMan.7.1.1
Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151)

Authors: Serge Abiteboul, Marcelo Arenas, Pablo Barceló, Meghyn Bienvenu, Diego Calvanese, Claire David, Richard Hull, Eyke Hüllermeier, Benny Kimelfeld, Leonid Libkin, Wim Martens, Tova Milo, Filip Murlak, Frank Neven, Magdalena Ortiz, Thomas Schwentick, Julia Stoyanovich, Jianwen Su, Dan Suciu, Victor Vianu, and Ke Yi

Published in: Dagstuhl Manifestos, Volume 7, Issue 1 (2018)

Abstract
The area of Principles of Data Management (PDM) has made crucial contributions to the development of formal frameworks for understanding and managing data and knowledge. This work has involved a rich cross-fertilization between PDM and other disciplines in mathematics and computer science, including logic, complexity theory, and knowledge representation. We anticipate on-going expansion of PDM research as the technology and applications involving data management continue to grow and evolve. In particular, the lifecycle of Big Data Analytics raises a wealth of challenge areas that PDM can help with. In this report we identify some of the most important research directions where the PDM community has the potential to make significant contributions. This is done from three perspectives: potential practical relevance, results already obtained, and research questions that appear surmountable in the short and medium term.
Cite as

Serge Abiteboul, Marcelo Arenas, Pablo Barceló, Meghyn Bienvenu, Diego Calvanese, Claire David, Richard Hull, Eyke Hüllermeier, Benny Kimelfeld, Leonid Libkin, Wim Martens, Tova Milo, Filip Murlak, Frank Neven, Magdalena Ortiz, Thomas Schwentick, Julia Stoyanovich, Jianwen Su, Dan Suciu, Victor Vianu, and Ke Yi. Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151). In Dagstuhl Manifestos, Volume 7, Issue 1, pp. 1-29, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)

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@Article{abiteboul_et_al:DagMan.7.1.1,
  author =	{Abiteboul, Serge and Arenas, Marcelo and Barcel\'{o}, Pablo and Bienvenu, Meghyn and Calvanese, Diego and David, Claire and Hull, Richard and H\"{u}llermeier, Eyke and Kimelfeld, Benny and Libkin, Leonid and Martens, Wim and Milo, Tova and Murlak, Filip and Neven, Frank and Ortiz, Magdalena and Schwentick, Thomas and Stoyanovich, Julia and Su, Jianwen and Suciu, Dan and Vianu, Victor and Yi, Ke},
  title =	{{Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151)}},
  pages =	{1--29},
  journal =	{Dagstuhl Manifestos},
  ISSN =	{2193-2433},
  year =	{2018},
  volume =	{7},
  number =	{1},
  editor =	{Abiteboul, Serge and Arenas, Marcelo and Barcel\'{o}, Pablo and Bienvenu, Meghyn and Calvanese, Diego and David, Claire and Hull, Richard and H\"{u}llermeier, Eyke and Kimelfeld, Benny and Libkin, Leonid and Martens, Wim and Milo, Tova and Murlak, Filip and Neven, Frank and Ortiz, Magdalena and Schwentick, Thomas and Stoyanovich, Julia and Su, Jianwen and Suciu, Dan and Vianu, Victor and Yi, Ke},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops-dev.dagstuhl.de/entities/document/10.4230/DagMan.7.1.1},
  URN =		{urn:nbn:de:0030-drops-86772},
  doi =		{10.4230/DagMan.7.1.1},
  annote =	{Keywords: database theory, principles of data management, query languages, efficient query processing, query optimization, heterogeneous data, uncertainty, knowledge-enriched data management, machine learning, workflows, human-related data, ethics}
}
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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