4th International Workshop on
Get proceedings at ceur-ws.org
Please join the workshop with ZOOM:
ZOOM LINK
The workshop will be fully virtual. We organized a zoom webinar which is accessible through Whova App or Whova Web.
See also the information at https://ecmlpkdd2020.net/attending/participate/
Science, technology, and commerce increasingly recognise the importance of machine 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 human 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 knowledge. 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 machine 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 different 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 (Workshop and Tutorial).
The workshop aims at discussing techniques and approaches for optimising 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 machine learning systems. Our objective is to bridge the communities researching and developing these techniques and systems in machine learning and data mining. 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.
In particular, we welcome contributions that address aspects including, but not limited to:
The full paper track covers new innovative contributions in the area of interactive adaptive learning. If you have a new method already evaluated briefly, a new tool to simplify interaction or some new insights the community might benefit from, please submit a regular paper. The page limit is 8-16 pages.
EasyChair Deadline: 9 June 2020
PDF Submission Deadline: 16 June 2020 (more details above)
The extended abstract track is ideal to discuss new ideas in the area of interactive adaptive learning. We encourage you to submit open challenges in research or industrial applications to initiate a discussion and find colleagues to collaborate with. The page limit is 2-4 pages.
EasyChair Deadline: 9 June 2020
PDF Submission Deadline: 16 June 2020 (more details above)
All accepted papers will be published at ceur-ws.org (indexed by e.g. google scholar) or within Springer LNCS proceedings depending on the number of submissions. Reviews are single-blind.
The paper must be be written in English and contain author names, affiliations, and email addresses. The paper must be in PDF using the LNCS format. See instructions here.
All accepted papers are presented in spotlight talks and/or poster sessions. At least one author of each accepted paper must be registered to the workshop.
Time | Program | Presenter/Author |
---|---|---|
10:00 - 11:30 | Session 1: | |
5m | Introduction | |
45m | Invited Talk: From Explainable AI to Human-Centered AI | Andreas Holzinger (Medical University Graz, Austria) |
20m | Improving Unsupervised Domain Adaptation with Representative Selection Techniques | I-Ting Chen and Hsuan-Tien Lin |
20m | On the Transferability of Deep Neural Networks for Recommender System | Duc Nguyen, Hao Niu, Kei Yonekawa, Mori Kurokawa, Chihiro Ono, Daichi Amagata, Takuya Maekawa and Takahiro Hara |
Break and Come Together | ||
12:00 - 13:25 | Session 2: | |
45m | Invited Talk: How to measure uncertainty in Uncertainty Sampling for Active Learning | Eyke Hüllermeier (University Paderborn, Germany) |
20m | Active Learning for LSTM-autoencoder-based Anomaly Detection in Electrocardiogram Readings | Tomáš Šabata and Martin Holena |
20m | Towards Landscape Analysis in Adaptive Learning of Surrogate Models | Zbyněk Pitra and Martin Holena |
Break and Come Together | ||
15:00 - 16:25 | Session 3: | |
45m | Invited Talk: The rapid growth of Human-in-the-Loop Machine Learning | Robert Munro (Machine Learning Consulting, USA) |
20m | Learning active learning at the crossroads? Evaluation and discussion | Louis Desreumaux and Vincent Lemaire |
20m | The Effects of Reluctant and Fallible Users in Interactive Online Machine Learning | Agnes Tegen, Paul Davidsson and Jan A. Persson |
Break and Come Together | ||
17:00 - 18:30 | Session 4: | |
45m | Invited Talk: When Humans and AI Collide | Kori Inkpen (Microsoft Research, USA) |
20m | VIAL-AD: Visual Interactive Labelling for Anomaly Detection - An approach and open research questions | Andreas Theissler, Anna-Lena Kraft, Max Rudeck and Fabian Erlenbusch |
20m | Get a Human-In-The-Loop: Feature Engineering via Interactive Visualizations | Dimitra Gkorou, Maialen Larranaga, Alexander Ypma, Faegheh Hasibi and Robert Jan van Wijk |
5m | Closing |
g.m.krempl (at) uu.nl
Utrecht University, Netherlands
vincent.lemaire (at) orange.com
Orange Labs, France
daniel.kottke (at) uni-kassel.de
University of Kassel, Germany
a.holzinger (at) hci-kdd.org
Medical University Graz, Austria
adrian.calma (at) uni-kassel.de
vencortex, Germany
polikar (at) rowan.edu
Rowan University, USA
bsick (at) uni-kassel.de
University of Kassel, Germany