2nd International Workshop on
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 labelled or unlabelled 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. Thereby, it continues the successful first workshop on Interactive Adaptive Learning at ECML PKDD 2017 in Skopje.
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 10 pages (excluding references and supplemental material).
Submission Deadline: 2 July 2018
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 pages (excluding references and supplemental material).
Submission Deadline: 23 July 2018
All accepted papers will be published at ceur-ws.org (indexed by e.g. google scholar). You are allowed to share, upload and distribute your paper. 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 |
---|---|---|
14:00 - 14:05 | Welcome | Organizing Committee |
14:05 - 14:30 | Introduction What is Interactive Adaptive Learning? |
Daniel Kottke |
14:30 - 15:15 | Session 1: Presentation of Full Papers | |
15m | On the Labeling Correctness in Computer Vision Datasets | Mohammed Al-Rawi and Dimosthenis Karatzas |
15m | Semi-supervised and Active Learning in Video Scene Classification from Statistical Features | Tomáš Šabata, Petr Pulc and Martin Holena |
15m | Active Stream Learning with an Oracle of Unknown Availability for Sentiment Prediction | Elson Serrao and Myra Spiliopoulou |
15:15 - 15:30 | Spotlights on Poster Session | |
3m | Human-Technology relations in a machine learning based commuter app | Lars Holmberg |
3m | An Interactive Learning Scenario for Real-time Environmental State Estimation Based on Heterogeneous and Dynamic Sensor Systems | Agnes Tegen, Paul Davidsson and Jan Persson |
3m | Adaptive Selection of Gaussian Process Model for Active Learning in Expensive Optimization | Jakub Repický, Zbyněk Pitra and Martin Holena |
3m | Towards Interactive Feature Selection with human-in-the-loop | Maialen Larranaga, Dimitra Gkorou, Thiago Guzella, Alexander Ypma, Faegheh Hasibi and Robert Jan van Wijk |
3m | Transfer of Knowledge for Surrogate Model Selection in Cost-Aware Optimization | Zbyněk Pitra, Jakub Repický and Martin Holena |
3m | Embodiment Adaptation from Interactive Trajectory Preferences | Michael Walton, Ben Migliori and John Reeder |
Start Morning Coffee Break | ||
15:30 - 16:30 | Poster Session Extended coffee break including poster session |
|
End Morning Coffee Break | ||
16:30 - 17:00 | Session 2: Presentation of Full Papers | |
15m | ActivMetal: Algorithm Recommendation with Active Meta Learning | Lisheng Sun-Hosoya, Isabelle Guyon and Michele Sebag |
15m | Alignment-Based Topic Extraction Using Word Embedding | Tyler Newman and Paul Anderson |
17:00 - 17:40 | Hot Topics in IAL Short ignite talks (including discussion) presenting recent challenges in IAL research. |
Vincent LemaireAndreas HolzingerGeorg Krempl |
georg.krempl (at) ovgu.de
University Magdeburg, Germany
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
polikar (at) rowan.edu
Rowan University, USA
bsick (at) uni-kassel.de
University of Kassel, Germany
acalma (at) uni-kassel.de
University of Kassel, Germany