IAL 2020
Interactive Adaptive Learning 2020

Table of Contents

• Front Matter
  
  
• Preface
  
  
• Table of Contents
  
  

Invited Talks

• When Humans and AI Collide 1
  Kori Inkpen
• The Rapid Growth of Human-in-the-Loop Machine Learning 2
  Robert Munro
• How to Measure Uncertainty in Uncertainty Sampling for Active Learning 3
  Eyke Hüllermeier
• From Explainable AI to Human-Centered AI 4
  Andreas Holzinger

Full Research Papers

• Improving Unsupervised Domain Adaptation with Representative Selection Techniques 5-21
  I-Ting Chen, Hsuan-Tien Lin
• On the Transferability of Deep Neural Networks for Recommender System 22-37
  Duc Nguyen, Hao Niu, Kei Yonekawa, Mori Kurokawa, Chihiro Ono, Daichi Amagata, Takuya Maekawa, Takahiro Hara
• Learning Active Learning at the Crossroads? Evaluation and Discussion 38-54
  Louis Desreumaux, Vincent Lemaire
• The Effects of Reluctant and Fallible Users in Interactive Online Machine Learning 55-71
  Agnes Tegen, Paul Davidsson, Jan A. Persson

Short Papers

• Active Learning for LSTM-autoencoder-based Anomaly Detection in Electrocardiogram Readings 72-77
  Tomáš Šabata, Martin Holeňa
• Towards Landscape Analysis in Adaptive Learning of Surrogate Models 78-83
  Zbyněk Pitra, Martin Holeňa
• VIAL-AD: Visual Interactive Labelling for Anomaly Detection - An Approach and Open Research Questions 84-89
  Andreas Theissler, Anna-Lena Kraft, Max Rudeck, Fabian Erlenbusch
• Get a Human-In-The-Loop: Feature Engineering via Interactive Visualizations 90-95
  Dimitra Gkorou, Maialen Larranaga, Alexander Ypma, Faegheh Hasibi, Robert Jan van Wijk