License: Creative Commons Attribution 3.0 Unported license (CC BY 3.0)
When quoting this document, please refer to the following
DOI: 10.4230/DagRep.10.4.1
URN: urn:nbn:de:0030-drops-137359
URL: https://drops.dagstuhl.de/opus/volltexte/2021/13735/
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Krempl, Georg ; Hofer, Vera ; Webb, Geoffrey ; Hüllermeier, Eyke
Weitere Beteiligte (Hrsg. etc.): Georg Krempl and Vera Hofer and Geoffrey Webb and Eyke Hüllermeier

Beyond Adaptation: Understanding Distributional Changes (Dagstuhl Seminar 20372)

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dagrep_v010_i004_p001_20372.pdf (3 MB)


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.

BibTeX - Entry

@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.dagstuhl.de/opus/volltexte/2021/13735},
  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}
}

Keywords: Statistical Machine Learning, Data Streams, Concept Drift, Non-Stationary Non-IID Data, Change Mining, Dagstuhl Seminar
Collection: Dagstuhl Reports, Volume 10, Issue 4
Issue Date: 2021
Date of publication: 15.03.2021


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