Workshop Interactive Adaptive Learning - ECML PKDD 2020

[vincent...@orange.com]

We organize a Workshop (Full Day) to be held at ECML PKDD 2020 on September 18, 2020 (or September 14, 2020) in Ghent, (Belgium)

 

  Interactive Adaptive Learning

  link : http://www.uni-kassel.de/go/ial2020 

 

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

 

Science, technology, and commerce increasingly recognize 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 optimize 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), ECML PKDD 2019 in Wurzburg (Germany)

 

The workshop aims at discussing techniques and approaches for optimizing 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.

 

 

We welcome submissions of *full papers* (8-16 pages) and *extended

abstracts* (2-4 pages). Each paper will be single-blinded peer- reviewed, and upon selection be presented and discussed at the workshop. For extended abstracts, works-in-progress or industrial experiences are welcome. At least one author of each accepted paper must be registered to the conference. 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. Please format your papers according to the LNCS format and submit them via EasyChair https://easychair.org/my/conference?conf=ial2020

 

*Key dates*

 

·        Paper Submission:     Monday, June 9, 2020

·        Author Notification:  Monday, July 09, 2020

·        Camera Ready:        Monday, July 28, 2020

·        Tutorial & Workshop (Full Day) : September 18, 2020 (or September 14, 2020)

 

We look forward to your contributions, the organizers, Adrian Calma, Andreas Holzinger, Daniel Kottke, Georg Krempl, Vincent Lemaire, Robi Polikar, Bernhard Sick

 

PS : 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 the page https://ecmlpkdd2020.net/attending/registration/

 

-------------------------------------

Vincent Lemaire - Orange Labs

Research Scientist - Data Mining

http://www.vincentlemaire-labs.fr

-------------------------------------

 

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