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|id=Vol-2444/alatiknow_3_contents
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-2444/ialatecml_3_contents.pdf
|volume=Vol-2444
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==None==
<pdf width="1500px">https://ceur-ws.org/Vol-2444/ialatecml_3_contents.pdf</pdf>
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                                        Table of Contents


Tutorial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    1
Foundations of Interactive Adaptive Learning
   Georg Krempl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .        1
From Interactive Machine Learning to Explainable Artificial Intelligence
   Andreas Holzinger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .           2

Invited Talk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .          3
Evaluation of Interactive Machine Learning Systems
   Nadia Boukhelifa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .           3

Full Research Papers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                      4
Toward Faithful Explanatory Active Learning with Self-explainable
   Neural Nets
   Stefano Teso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .      4
Validating One-Class Active Learning with User Studies – a Prototype
   and Open Challenges
   Holger Trittenbach, Adrian Englhardt and Klemens Böhm . . . . . . . . . . .                                          17
RAL – Improving Stream-Based Active Learning by Reinforcement
   Learning
   Sarah Wassermann, Thibaut Cuvelier and Pedro Casas . . . . . . . . . . . . .                                          32
Knowledge-based Selection of Gaussian Process Surrogates
   Zbyněk Pitra, Lukáš Bajer and Martin Holeňa . . . . . . . . . . . . . . . . . . . . .                             48
Explicit Control of Feature Relevance and Selection Stability Through
   Pareto Optimality
   Victor Hamer and Pierre Dupont . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .                        64
Deep Bayesian Semi-Supervised Active Learning for Sequence Labelling
   Tomáš Šabata, Juraj Eduard Páll and Martin Holeňa . . . . . . . . . . . . . . .                                  80

Short Papers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
Combating Stagnation in Reinforcement Learning Through ‘Guided
   Learning’ with ‘Taught-Respone Memory’
   Keith Tunstead and Joeran Beel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96
Towards Active Simulation Data Mining
   Mirko Bunse, Amal Saadallah and Katharina Morik . . . . . . . . . . . . . . . . 104
Active Feature Acquistion for Opinion Stream Classification under
   Drift
   Ranjith Shivakumaraswamy, Christian Beyer, Vishnu Unnikrishnan,
   Eirini Ntoutsi and Myra Spiliopoulou . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

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