Beyond Adaptation: Understanding Distributional Changes, Wadern 2020

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 black box data generator., to which models are adapted (Figure 1, left).

Dagstuhl Seminar 2020 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.