| Vol-3079 urn:nbn:de:0074-3079-5 Copyright © 2021 for the individual papers by the papers' authors. Copyright © 2021 for the volume as a collection by its editors. This volume and its papers are published under the Creative Commons License Attribution 4.0 International (CC BY 4.0).

# IAL 2021
Interactive Adaptive Learning 2021

### Proceedings of the Workshop on Interactive Adaptive Learning (IAL 2021)
co-located with the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2021)

### Bilbao, Spain, September 13, 2021.

Edited by

### Georg Krempl *
Vincent Lemaire **
Daniel Kottke ***
Andreas Holzinger ****
Barbara Hammer *****

* Utrecht University, Faculty of Science, 3512 JE Utrecht, Netherlands
** Orange Labs, 22300 Lannion, France
*** University of Kassel, Faculty of Electrical Engineering and Computer Science, 34121 Kassel, Germany
**** Graz University of Technology, Institute of Interactive Systems & Data Science, 8010 Graz, Austria
***** University of Bielefeld, CITEC Center of Excellence, Inspiration 1, 33619 Bielefeld, Germany

## Table of Contents

- Front Matter
- Preface
  Summary: There were 13 papers submitted for peer-review to this workshop. Out of these, 10 papers were accepted for this volume.
- Table of Contents

### Research Papers

- MetaREVEAL: RL-based Meta-learning from Learning Curves 1-20
  Manh Hung Nguyen, Isabelle Guyon, Lisheng Sun-Hosoya, Nathan Grinsztajn
- Uncertainty and Utility Sampling with Pre-Clustering 21-34
  Zhixin Huang, Yujiang He, Stephan Vogt, Bernhard Sick
- Evidential Nearest Neighbours in Active Learning 35-49
  Daniel Zhu, Arnaud Martin, Yolande Le Gall, Jean-Christophe Dubois, Vincent Lemaire
- SLAYER: A Semi-supervised Learning Approach for Drifting Data Streams under Extreme Verification Latency 50-64
  Maria Arostegi, Jesus Lobo, Javier Del Ser
- A Concept for Highly Automated Pre-Labeling via Cross-Domain Label Transfer for Perception in Autonomous Driving 65-69
  Maarten Bieshaar, Marek Herde, Denis Huselijc, Bernhard Sick
- Active Class Selection with Uncertain Class Proportions 70-79
  Mirko Bunse, Katharina Morik
- Sample Noise Impact on Active Learning 80-88
  Alexandre Abraham, Léo Dreyfus-Schmidt
- Contrastive Representations for Label Noise Require Fine-Tuning 89-104
  Pierre Nodet, Vincent Lemaire, Alexis Bondu, Antoine Cornuéjols
- Combining Gaussian Processes with Neural Networks for Active Learning in Optimization 105-120
  Jiří Růžička, Jan Koza, Jiří Tumpach, Zbyněk Pitra, Martin Holena
- Stochastic Adversarial Gradient Embedding for Active Domain Adaptation 121-137
  Victor Bouvier, Philippe Very, Clément Chastagnol, Myriam Tami, Hudelot Céline

2022-01-24: submitted by Daniel Kottke, metadata incl. bibliographic data published under Creative Commons CC0
2022-01-25: published on CEUR Workshop Proceedings (CEUR-WS.org, ISSN 1613-0073) |valid HTML5|