4th International Workshop on

Interactive Adaptive Learning (IAL2020)

Co-Located With ECML PKDD 2020

14 September 2020 - Ghent (Belgium)

Image © by Michael Schmalenstroer (CC-BY-SA-3.0)

Due to the current uncertainty caused by COVID-19, in case authors cannot attend the venue in person we will prepare alternative means of presentation via videoconferencing.
See also the information at https://ecmlpkdd2020.net/attending/registration/

Topic

Science, technology, and commerce increasingly recognise the importance of machine learning approaches for data-intensive, evidence-based decision making.

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:

    Novel Techniques for Active, Semi-Supervised, Transfer Learning
  • methods for big, evolving, or streaming data,
  • methods for recent complex model structures such as deep learning neural networks or recurrent neural networks,
  • methods for interacting with imperfect or multiple oracles, e.g. learning from crowds,
  • methods for incorporating domain knowledge and constraints,
  • methods for timing the interaction and for combining different types of information,
  • online and ensemble methods for evolving models and systems, with specific switching and fusion techniques, and (inter-)active data integration techniques,
  • Innovative Use and Applications of Active, Semi-Supervised, Transfer Learning
  • for filtering, forgetting, resampling,
  • for active class or feature selection, e.g. from multi-modal data,
  • for detection of change, outliers, frauds, or attacks,
  • new interactive learning protocols and application scenarios, e.g., brain-computer interfaces, crowdsourcing, ...
  • in application in data-intensive science,
  • in applications with real-world deployment,
  • Techniques for Combined Interactive Adaptive Learning
  • methods combining adaptive, active, semi-supervised, or transfer learning techniques,
  • cost-aware methods and methods for estimating the impact of employing additional resources, such as data or processing capacities, on the learning progress,
  • methodologies for the evaluation of such techniques, and comparative studies,
  • methods for automating the control of an interactive adaptive learning process.

Important dates

The following timeline shows the most important dates for the workshop.

  • Submission open

    18 April 2020

    You can submit your contributions via EasyChair.

  • Submission deadline UPDATE

    9 June 2020 (EasyChair paper registration)
    16 June 2020 (PDF submission)

    Due to many requests, we decided to postpone the deadline for pdf upload for both tracks. Note, that it is still required to register the paper in EasyChair with authors, title, and abstract until 9 June.
  • Notification EXTENDED

    9 July 2020
    16 July 2020

  • Camera Ready EXTENDED

    28 July 2020
    4 August 2020

  • Workshop (Full Day)

    14 September 2020

    Co-Located With The The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML PKDD 2020).

Submit your contribution

You can submit your contribution via EasyChair. Please use the link below.

Full Paper Track

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)

Extended Abstract Track

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)

Indexed Publishing

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.

LNCS Style

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.

Presentation

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.

Invited Talks

to be announced

Program

to be determined

Committee

Organizing Committee:
ial2020 (at) easychair.org

Georg Krempl

g.m.krempl (at) uu.nl
Utrecht University, Netherlands

Vincent Lemaire

vincent.lemaire (at) orange.com
Orange Labs, France

Daniel Kottke

daniel.kottke (at) uni-kassel.de
University of Kassel, Germany

Andreas Holzinger

a.holzinger (at) hci-kdd.org
Medical University Graz, Austria

Adrian Calma

adrian.calma (at) uni-kassel.de
vencortex, Germany

Steering Committee:

Robi Polikar

polikar (at) rowan.edu
Rowan University, USA

Bernhard Sick

bsick (at) uni-kassel.de
University of Kassel, Germany

Program Committee (tentative):

Albert Bifet (LTCI, Telecom ParisTech)
Alexis Bondu (Orange Labs)
Klemens Böhm (Karlsruhe Institute of Technology)
Martin Holena (Institute of Computer Science)
Dino Ienco (IRSTEA)
George Kachergis
Edwin Lughofer (Johannes Kepler University Linz)
Ingo Scholtes (University of Zurich)
Stefano Teso (Katholieke Universiteit Leuven)
Holger Trittenbach (Karlsruhe Institute of Technology)
Sebastian Tschiatschek