3rd International Tutorial & Workshop on
Get proceedings at ceur-ws.org
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 and tutorial 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 & Tutorial), IJCNN 2018 in Rio (Tutorial), and ECML PKDD 2018 in Dublin (Workshop). (Links to be completed)
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.
Submission Deadline: 28 June 2019
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.
Submission Deadline: 28 June 2019
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 |
---|---|---|
9:00 - 10:30 | Tutorial (Pt. 1): | |
Foundations of Interactive Adaptive Learning [slides] | Georg Krempl | |
Morning Coffee and Tea Break with Poster Session | ||
11:00 - 12:30 | Tutorial (Pt. 2): | |
From Interactive ML to Explainable AI [slides] | Andreas Holzinger | |
12:30 - 12:40 | Spotlight Presentations of Posters | see below |
Lunch Break at conference venue with Poster Session | ||
13:45 - 13:50 | Workshop: Introduction | Daniel Kottke |
13:50 - 14:05 | Session 1: Presentation of Full Papers | |
15m | Toward Faithful Explanatory Active Learning with Self-explainable Neural Nets [slides] | Stefano Teso |
14:05 - 15:00 | Invited Talk with Q&A on Evaluation of Interactive Machine Learning Systems [slides] | Nadia Boukhelifa |
Coffee & Tea Break with Poster Session | ||
15:20 - 16:35 | Session 2: Presentation of Full Papers | |
15m | Validating One-Class Active Learning with User Studies - a Prototype and Open Challenges [poster] [slides] | Holger Trittenbach, Adrian Englhardt, Klemens Böhm |
15m | RAL - Improving Stream-Based Active Learning by Reinforcement Learning | Sarah Wassermann, Thibaut Cuvelier, Pedro Casas |
15m | Knowledge-based Selection of Gaussian Process Surrogates | Zbyněk Pitra, Lukáš Bajer, Martin Holena |
15m | Explicit Control of Feature Relevance and Selection Stability Through Pareto Optimality [poster] [slides] | Victor Hamer, Pierre Dupont |
15m | Deep Bayesian Semi-Supervised Active Learning for Sequence Labelling [poster] [slides] | Tomáš Šabata, Juraj Eduard Páll and Martin Holeňa |
16:35 - 16:45 | Workshop: Closing | Daniel Kottke |
Special transport to opening ceremony for participants of this workshop. |
Short Paper | Presenter/Author |
---|---|
Combating Stagnation in Reinforcement Learning Through 'Guided Learning' With 'Taught-Response Memory' [poster] | Keith Tunstead, Joeran Beel |
Towards Active Simulation Data Mining [poster] | Mirko Bunse, Amal Saadallah and Katharina Morik |
Active Feature Acquisition For Opinion Stream Classification Under Drift [poster] | Ranjith Shivakumaraswamy, Christian Beyer, Vishnu Unnikrishnan, Eirini Ntoutsi, Myra Spiliopoulou |
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
Darwin, USA
polikar (at) rowan.edu
Rowan University, USA
bsick (at) uni-kassel.de
University of Kassel, Germany