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