September 6 – 11 , 2020, Dagstuhl Seminar 20372

Beyond Adaptation: Understanding Distributional Changes


Niall Adams (Imperial College London, GB)
Vera Hofer (Universität Graz, AT)
Eyke Hüllermeier (Universität Paderborn, DE)
Georg Krempl (Utrecht University, NL)
Geoffrey Webb (Monash University – Clayton, AU)

For support, please contact

Annette Beyer for administrative matters

Michael Gerke for scientific matters


List of Participants
Shared Documents
Dagstuhl Seminar Schedule [pdf]


The world is dynamically changing and non-stationary. This is reflected by the variety of methods that have been developed in statistics, machine learning, and data mining to detect these changes, and to adapt to them. Nevertheless, most of this research views the changing environment as a black box data generator, to which models are adapted (Figure 1, left).

Figure 1

This Dagstuhl Seminar takes a change mining point of view, by focusing on change as a research subject of its own. This aims to make the distributional change process in the data generating environment transparent (Figure 1, right). It seeks to establish a better understanding of the causes, nature and consequences of distributional changes. Thereby, it aims to address the following research questions:

  1. Understanding which scenarios and types of change are relevant in practical applications
  2. How to model such types of change effectively
  3. How to detect, verify, and measure types of change
  4. How to establish bounds for distributional change, or for predictive performance under change
  5. How to effectively adapt prediction models to the different types of change
  6. How to visualise change, and how to highlight individual types of change
  7. How to evaluate techniques for the above questions

Thereby, this seminar will bridge communities where in separate lines of research some parts of these questions are already studied. These include data stream mining, where 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, adversarial generators in adversarial machine learning, and the evolving and adaptive systems community. Therefore, this seminar aims to bring together researchers and practitioners from these different areas, and to stimulate research towards a thorough understanding of distributional changes.

Motivation text license
  Creative Commons BY 3.0 DE
  Niall Adams, Vera Hofer, Eyke Hüllermeier, Georg Krempl, and Geoffrey Webb


  • Artificial Intelligence / Robotics


  • Statistical machine learning
  • Data streams
  • Concept drift
  • Non-stationary non-iid data
  • Change mining


In the series Dagstuhl Reports each Dagstuhl Seminar and Dagstuhl Perspectives Workshop is documented. The seminar organizers, in cooperation with the collector, prepare a report that includes contributions from the participants' talks together with a summary of the seminar.


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Furthermore, a comprehensive peer-reviewed collection of research papers can be published in the series Dagstuhl Follow-Ups.

Dagstuhl's Impact

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