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                                  Preface


Science, technology, and commerce increasingly recognise the importance of ma-
chine learning approaches for data-intensive, evidence-based decision making.
This is accompanied by increasing numbers of machine learning applications
and volumes of data. Nevertheless, the capacities of processing systems or hu-
man supervisors or domain experts remain limited in real-world applications.
Furthermore, many applications require fast reaction to new situations, which
means that first predictive models need to be available even if little data is
yet available. Therefore approaches are needed that optimise the whole learning
process, including the interaction with human supervisors, processing systems,
and data of various kind and at different timings: techniques for estimating the
impact of additional resources (e.g. data) on the learning progress; techniques
for the active selection of the information processed or queried; techniques for
reusing knowledge across time, domains, or tasks, by identifying similarities and
adaptation to changes between them; techniques for making use of different types
of information, such as labeled or unlabeled data, constraints or domain knowl-
edge. Such techniques are studied for example in the fields of adaptive, active,
semi-supervised, and transfer learning. However, this is mostly done in separate
lines of research, while combinations thereof in interactive and adaptive ma-
chine learning systems that are capable of operating under various constraints,
and thereby address the immanent real-world challenges of volume, velocity and
variability of data and data mining systems, are rarely reported. Therefore, this
workshop aims to bring together researchers and practitioners from these differ-
ent areas, and to stimulate research in interactive and adaptive machine learning
systems as a whole. It continues a successful series of events at ECML PKDD
2017 in Skopje (Workshop and Tutorial), IJCNN 2018 in Rio (Tutorial), ECML
PKDD 2018 in Dublin (Workshop), and ECML PKDD 2019 in Würzburg (Work-
shop and Tutorial).
The workshop aims at discussing techniques and approaches for optimising the
whole learning process, including the interaction with human supervisors, pro-
cessing systems, and includes adaptive, active, semi-supervised, and transfer
learning techniques, and combinations thereof in interactive and adaptive ma-
chine learning systems. Our objective is to bridge the communities researching
and developing these techniques and systems in machine learning and data min-
ing. Therefore, we welcome contributions that present a novel problem setting,
propose a novel approach, or report experience with the practical deployment of
such a system and raise unsolved questions to the research community.
II     Preface

All in all, we accepted four regular papers (4 papers submitted) and four short
papers (6 submitted) to be published in these workshop proceedings. The au-
thors discuss approaches, identify challenges and gaps between active learning
research and meaningful applications, as well as define new application-relevant
research directions. We thank the authors for their submissions and the program
committee for their hard work.

September 2020                              Adrian Calma, Andreas Holzinger
                               Daniel Kottke, Georg Krempl, Vincent Lemaire
                         Organization


Organizing Committee
Adrian Calma
Andreas Holzinger         Graz University of Technology
Daniel Kottke             University of Kassel
Georg Krempl              Utrecht University
Vincent Lemaire           Orange Labs France

Program Committee
Albert Bifet              LTCI, Telecom ParisTech
Alexis Bondu              Orange Labs
Klemens Böhm             Karlsruhe Institute of Technology
Martin Holena             Institute of Computer Science
Dino Ienco                IRSTEA
George Kachergis
Edwin Lughofer            Johannes Kepler University Linz
Shreyasi Pathak           University of Twente
Ingo Scholtes             University of Zurich
Carlos Soares             LIAAD-INESCTEC, Porto
Stefano Teso              Katholieke Universiteit Leuven
Holger Trittenbach        Karlsruhe Institute of Technology
Sebastian Tschiatschek

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