Preface
Science, technology, and commerce increasingly recognize the importance of ma-
chine learning approaches for data-intensive, evidence-based decision making.
     This is accompanied by increasing numbers of machine learning applica-
tions and volumes of data. Nevertheless, the capacities of processing systems
or human supervisors or domain experts remain limited in real-world applica-
tions. 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 optimize 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
combined tutorial and workshop aims to bring together researchers and practi-
tioners from these different areas, and to stimulate research in interactive and
adaptive machine learning systems as a whole.
     This workshop aims at discussing techniques and approaches for optimizing
the whole learning process, including the interaction with human supervisors,
processing 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.
     All in all, we accepted five regular papers (7 papers submitted) and 3 short
papers (4 submitted) to be published in these workshop proceedings. The authors
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 2017                     Georg Krempl, Vincent Lemaire, Robi Polikar
                                   Bernhard Sick, Daniel Kottke, Adrian Calma


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Organizing Committee
Georg Krempl, Otto von Guericke University
Vincent Lemaire, Orange Labs France
Robi Polikar, Rowan University
Bernhard Sick, University of Kassel
Daniel Kottke, University of Kassel
Adrian Calma, University of Kassel


Program Committee
Michael Beigl, KIT
Giacomo Boracchi, Politecnico di Milano
Bartosz Krawczyk, Virginia Commonwealth University
Mark Embrechts, Rensselaer Polytechnic Institute
Michael Granitzer, University of Passau
Barbara Hammer, University of Bielefeld
Henner Heck, University of Kassel
Vera Hofer, University of Graz
George Kachergis, Radboud University
Christian Müller-Schloer, University of Hannover
Christin Seifert, TU Dresden
Ammar Shaker, University of Paderborn
Jasmina Smailovic, Jožef Stefan Institute
Myra Spiliopoulou, Otto von Guericke University
Jurek Stefanowski, University of Poznan
Dirk Tasche, Swiss Financial Market Supervisory Authority FINMA
Martin Znidarsic, Jožef Stefan Institute




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