Programme

Keynote speakers

    Dov M. Gabbay
University of Luxembourg, Luxembourg
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    Simon Hutteger
University of California, USA
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    Ute Schmid
University of Bamberg, Germany
Title:Making Humans and Machines Learn from Each Other
Abstract:

Inductive Logic Programming (ILP) is introduced as a highly expressive approach to machine learning (ML). Together with regression models and decision tree algorithms, ILP belongs to the class of interpretable ML approaches -- that is, the classification hypothesis (called a model in the context of ML) induced from examples is expressed in a symbolic format. In contrast to classic ML where instances mostly represented by feature vectors, ILP can be applied to relational data. In contrast to end-to-end learning, learning from examples represented by features or relations is data parsimonious. After a short introduction into the history of ML, I will show how ILP can be applied to interesting real-world domains to learn complex rules involving variables and recursion. Furthermore, I will give examples how ILP can be used as surrogate model to explain blackbox classifiers which are learned with (deep) neural network approaches. I will argue that presenting either visualisations or rules to a user will often not suffice as a helpful explanation. Instead, I will propose a variety of textual, visual, and example-based explanations. Furthermore, I will discuss that explanations are not "one size fits all" but that it depends on the user, the problem, and the current situation which explanation is most helpful. Finally, I will present a new method which allows the machine learning system to exploit not only class corrections but also explanations from the user to correct and adapt learned models in interactive learning scenarios.

    Luc De Raedt
KU Leuven, Belgium
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    Claudia D'Amato
Università degli Studi di Bari, Italy
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Accepted papers

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Detailed programme

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