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https://ceur-ws.org/Vol-3770/preface.pdf
Interactive Adaptive Learning 2024
Workshop Proceedings
Mirko Bunse1 , Marek Herde2 , Georg Krempl3 , Vincent Lemaire4 , Minh Tuan Pham2 ,
Amal Saadallah1 and Alaa Tharwat5
1
Lamarr Institute for Machine Learning and Artificial Intelligence, Germany
2
University of Kassel, Germany
3
Utrecht University, Netherlands
4
Orange Innovation, France
5
Hochschule Bielefeld, Germany
Abstract
This document is the preface of the proceedings of the 8th International Workshop & Tutorial on Interactive
Adaptive Learning, held on September 9th , 2024 in Vilnius, Lithuania. We received 11 submissions for peer-review,
out of which we accepted 8 papers for this volume. In addition, we publish an extended abstract of the tutorial
that we give as a part of the workshop program.
Preface
Methods of machine learning are approaching their limits whenever training data of a high quality are
scarce. The potential reasons for data scarcity are manifold: limited capabilities of human supervisors
and processing systems, a need for early predictions which can later be refined, or transfer settings
where the only available data stem from some different learning task.
Situations like these demand methods that improve the overall life-cycle of machine learning models,
including interactions with human supervisors, interactions with other processing systems, and adapta-
tions to different forms of data that become available at different points in time. This demand includes
techniques for evaluating the impact of additional resources (e.g., data) on the learning process; strate-
gies for actively selecting information to be processed or queried; techniques for reusing knowledge
over time, across different domains or tasks, by recognizing similarities and by adapting to changes; and
methods for effectively using different types of information, like labeled and unlabeled data, constraints,
and knowledge. Techniques of this kind are being investigated, for example, in the areas of adaptive,
active, semi-supervised, and transfer learning. While these investigations often happen in isolation of
each other, real use cases of machine learning require interactive and adaptive systems that operate
under changing conditions and address the challenges of volume, velocity, and variability of the data.
This combination of a workshop and tutorial continues to stimulate research on systems that combine
multiple areas of interactive and adaptive machine learning, by bringing together researchers and
practitioners from these different areas. We have welcomed contributions that present a novel problem,
propose a new approach, report practical experience with such a system, or raise open questions
for the research community. This edition of the Interactive Adaptive Learning workshop, which is
co-located with ECML-PKDD, continues a successful series of events, including a workshop & tutorial
at ECML-PKDD 2023 in Torino, a workshop at ECML-PKDD 2022 in Grenoble, a workshop at the virtual
ECML-PKDD 2021, a workshop at the virtual ECML-PKDD 2020, a workshop & tutorial at ECML-PKDD
IAL@ECML-PKDD’24: 8th Intl. Worksh. & Tutorial on Interactive Adaptive Learning, Sep. 9th , 2024, Vilnius, Lithuania
Envelope-Open mirko.bunse@cs.tu-dortmund.de (M. Bunse); marek.herde@uni-kassel.de (M. Herde); g.m.krempl@uu.nl (G. Krempl);
vincent.lemaire@orange.com (V. Lemaire); tuan.pham@uni-kassel.de (M. T. Pham); amal.saadallah@cs.tu-dortmund.de
(A. Saadallah); alaa.othman@fh-bielefeld.de (A. Tharwat)
Orcid 0000-0002-5515-6278 (M. Bunse); 0000-0002-4153-2594 (G. Krempl); 0000-0002-6030-2356 (V. Lemaire);
0000-0003-2976-7574 (A. Saadallah)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
i
Mirko Bunse et al. CEUR Workshop Proceedings i–iii
2019 in Würzburg, a workshop at ECML-PKDD 2018 in Dublin, a tutorial at IJCNN 2018 in Rio, and a
workshop & tutorial at ECML-PKDD 2017 in Skopje.
This year, we accepted 8 papers out of 11 submissions for their publication in these workshop
proceedings. In addition to these contributions, we publish an extended abstract of a tutorial that
belongs to the workshop program. We thank all authors for their valuable submissions and all members
of the program committee for their great support.
August 2024 Mirko Bunse, Marek Herde, Georg Krempl, Vincent Lemaire,
Minh Tuan Pham, Amal Saadallah, and Alaa Tharwat
Organization
Organizing Committee
Mirko Bunse Lamarr Institute for Machine Learning and Artificial Intelligence,
Germany
Marek Herde University of Kassel, Germany
Georg Krempl Utrecht University, Netherlands
Vincent Lemaire Orange Innovation, France
Minh Tuan Pham University of Kassel, Germany
Amal Saadallah Lamarr Institute for Machine Learning and Artificial Intelligence,
Germany
Alaa Tharwat Hochschule Bielefeld, Germany
Steering Committee
Adrian Calma University of Kassel, Germany
Barbara Hammer Bielefeld University, Germany
Andreas Holzinger University of Natural Resources and Life Sciences Vienna, Austria
Daniel Kottke Deutsche Bahn, Germany
Robi Polikar Rowan University, USA
Bernhard Sick University of Kassel, Germany
Program Committee
Alexandre Abraham Implicity, France
Christian Beyer Otto-von-Guericke University Magdeburg, Germany
Alexis Bondu Orange Labs, France
Michiel Bron Utrecht University, Netherlands
Mirko Bunse Lamarr Institute for Machine Learning and Artificial Intelligence, Ger-
many
Michael Granitzer University of Passau, Germany
Barbara Hammer Bielefeld University, Germany
Marek Herde University of Kassel, Germany
Martin Holeňa Czech Academy of Sciences, Czech Republic
Andreas Holzinger University of Natural Resources and Life Sciences Vienna, Austria
ii
Mirko Bunse et al. CEUR Workshop Proceedings i–iii
Bernhard Pfahringer University of Waikato
Minh Tuan Pham University of Kassel, Germany
Amal Saadallah Lamarr Institute for Machine Learning and Artificial Intelligence, Ger-
many
Yvan Saeys Ghent University, Belgium
Myra Spiliopoulou University of Magdeburg, Germany
Alaa Tharwat Hochschule Bielefeld, Germany
Andreas Theissler Aalen University of Applied Sciences, Germany
Contents
Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i–iii
Mirko Bunse, Marek Herde, Georg Krempl, Vincent Lemaire, Minh Tuan Pham, Amal Saadallah, and
Alaa Tharwat
Extended Abstracts
Tutorial: Interactive Adaptive Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1–6
Marek Herde, Minh Tuan Pham, Alaa Tharwat, and Bernhard Sick
Deep Transfer Hashing for Adaptive Learning on Federated Streaming Data . . . . . . . . . . . . . . . . . . 7–11
Manuel Röder and Frank-Michael Schleif
Towards Deep Active Learning in Avian Bioacoustics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12–17
Lukas Rauch, Denis Huseljic, Moritz Wirth, Jens Decke, Bernhard Sick, and Christoph Scholz
Research Papers
Amortized Active Learning for Nonparametric Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18–32
Cen-You Li, Marc Toussaint, Barbara Rakitsch, and Christoph Zimmer
General Reusability: Ensuring Long-Term Benefits of Deep Active Learning . . . . . . . . . . . . . . . . . 33–46
Paul Hahn, Denis Huseljic, Marek Herde, and Bernhard Sick
Suitability of Modern Neural Networks for Active and Transfer Learning in Surrogate-Assisted Black-Box
Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47–67
Martin Holeňa and Jan Koza
Active Learning with Physics-Informed Graph Neural Networks on Unstructured Meshes . . . . 68–76
Jens Decke, Alexander Heinen, Bernhard Sick, and Christian Gruhl
Combining Large Language Model Classifications and Active Learning for Improved Technology-
Assisted Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77–95
Michiel P. Bron, Berend Greijn, Bruno Messina Coimbra, Rens van de Schoot, and Ayoub Bagheri
Contextual kNN Ensemble Retrieval Approach for Semantic Postal Address Matching . . . . . . . 96–111
El Moundir Faraoun, Nédra Mellouli, Stéphane Millot, and Myriam Lamolle
iii