IAL 2019
Interactive Adaptive Learning

Table of Contents

• Front Matter
  
  
• Preface
  
  
• Table of Contents
  
  

Tutorials

• Foundations of Interactive Adaptive Learning 1
  Georg Krempl
• From Interactive Adaptive Learning 2
  Andreas Holzinger

Invited Talk

• Evaluation of Interactive Machine Learning Systems 3
  Nadia Boukhelifa

Full Research Papers

• On the Labeling Correctness in Computer Vision Datasets 4-16
  Stefan Teso
• Validating One-Class Active Learning with User Studies － a Prototype and Open Challenges 17-31
  Holger Trittenbach, Adrian Engelhardt, Klemens Böhm
• RAL － Improving Stream-Based Active Learning by Reinforcement Learning 32-47
  Sarah Wassermann Thibaut Cuvelier Pedro Casas
• Knowledge-Based Selection of Gaussian Process Surrogates 48-63
  Zbyněk Pitra, Lukáš Bajer, Martin Holeňa
• Explicit Control of Feature Relevance and Selection Stability Through Pareto Optimality 64-79
  Victor Hamer, Pierre Dupont
• Deep Bayesian Semi-Supervised Active Learning for Sequence Labelling 80-95
  Tomáš Šabata, Juraj Eduard Páll Martin Holeňa

Short Papers

• Combating Stagnation in Reinforcement Learning Through ’Guided Learning’ with ’Taught-Respone Memory’ 96-103
  Keith Tunstead Joeran Beel
• Towards Active Simulation Data Mining 104-107
  Mirko Bunse, Amal Saadallah, Katharina Morik
• Active Feature Acquistion for Opinion Stream Classification under Drift 108-111
  Ranjith Shivakumaraswamy, Christian Beyer, Vishnu Unnikrishnan Eirini Ntoutsi Myra Spiliopoulou