Probabilistic Active Learning: Towards Combining Versatility, Optimality and Efficiency. - Georg Krempl; Daniel Kottke; Myra Spiliopoulou (Discovery Science, 2014)

Probabilistic Active Learning: A Short Proposition. - Georg Krempl; Daniel Kottke; Myra Spiliopoulou (ECAI, 2014)

Data-Driven Spine Detection for Multi-Sequence MRI. - Daniel Kottke; Gino Gulamhussene; Klaus D. Tönnies (Bildverarbeitung für die Medizin, 2015)

Probabilistic Active Learning in Datastreams. - Daniel Kottke; Georg Krempl; Myra Spiliopoulou (IDA, 2015)

Optimised probabilistic active learning (OPAL) - For fast, non-myopic, cost-sensitive active classification. - Georg Krempl; Daniel Kottke; Vincent Lemaire 001 (Mach. Learn., 2015)

Multi-Class Probabilistic Active Learning. - Daniel Kottke; Georg Krempl; Dominik Lang; Johannes Teschner; Myra Spiliopoulou (ECAI, 2016)

Investigating Exploratory Capabilities of Uncertainty Sampling using SVMs in Active Learning. - Dominik Lang; Daniel Kottke; Georg Krempl; Myra Spiliopoulou (AL@iKNOW, 2016)

Challenges of Reliable, Realistic and Comparable Active Learning Evaluation. - Daniel Kottke; Adrian Calma; Denis Huseljic; Georg Krempl; Bernhard Sick (IAL@PKDD/ECML, 2017)

Probabilistic Active Learning with Structure-Sensitive Kernels. - Dominik Lang; Daniel Kottke; Georg Krempl (IAL@PKDD/ECML, 2017)

Learning to Learn: Dynamic Runtime Exploitation of Various Knowledge Sources and Machine Learning Paradigms. - Adrian Calma; Daniel Kottke; Bernhard Sick; Sven Tomforde (FAS*W@SASO/ICCAC, 2017)

Active Learning With Realistic Data - A Case Study. - Adrian Calma; Moritz Stolz; Daniel Kottke; Sven Tomforde; Bernhard Sick (IJCNN, 2018)

Active Sorting - An Efficient Training of a Sorting Robot with Active Learning Techniques. - Marek Herde; Daniel Kottke; Adrian Calma; Maarten Bieshaar; Stephan Deist; Bernhard Sick (IJCNN, 2018)

The Other Human in The Loop - A Pilot Study to Find Selection Strategies for Active Learning. - Daniel Kottke; Adrian Calma; Denis Huseljic; Christoph Sandrock; George Kachergis; Bernhard Sick (IJCNN, 2018)

Combining Self-reported Confidences from Uncertain Annotators to Improve Label Quality. - Christoph Sandrock; Marek Herde; Adrian Calma; Daniel Kottke; Bernhard Sick (IJCNN, 2019)

Limitations of Assessing Active Learning Performance at Runtime. - Daniel Kottke; Jim Schellinger; Denis Huseljic; Bernhard Sick (CoRR, 2019)

Multi-Annotator Probabilistic Active Learning. - Marek Herde; Daniel Kottke; Denis Huseljic; Bernhard Sick (ICPR, 2020)

Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks. - Denis Huseljic; Bernhard Sick; Marek Herde; Daniel Kottke (ICPR, 2020)

Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning. - Tuan Pham Minh; Daniel Kottke; Anna Tsarenko; Christian Gruhl; Bernhard Sick (IJCNN, 2020)

Statistical Analysis of Pairwise Connectivity. - Georg Krempl; Daniel Kottke; Tuan Pham (DS, 2021)

Probabilistic Active Learning for Active Class Selection. - Daniel Kottke; Georg Krempl; Marianne Stecklina; Cornelius Styp von Rekowski; Tim Sabsch; Tuan Pham Minh; Matthias Deliano; Myra Spiliopoulou; Bernhard Sick (CoRR, 2021)

ACE: A Novel Approach for the Statistical Analysis of Pairwise Connectivity. - Georg Krempl; Daniel Kottke; Tuan Pham Minh (CoRR, 2021)

Toward optimal probabilistic active learning using a Bayesian approach. - Daniel Kottke; Marek Herde; Christoph Sandrock; Denis Huseljic; Georg Krempl; Bernhard Sick (Mach. Learn., 2021)

A holistic, decision-theoretic framework for pool-based active learning. - Daniel Kottke (2021)

Ein holistischer Ansatz für Pool-basiertes Aktives Lernen. - Daniel Kottke (Ausgezeichnete Informatikdissertationen, 2021)

A Stopping Criterion for Transductive Active Learning. - Daniel Kottke; Christoph Sandrock; Georg Krempl; Bernhard Sick (ECML/PKDD (4), 2022)

Fast Bayesian Updates for Deep Learning with a Use Case in Active Learning. - Marek Herde; Zhixin Huang; Denis Huseljic; Daniel Kottke; Stephan Vogt; Bernhard Sick (CoRR, 2022)

Efficient SVDD sampling with approximation guarantees for the decision boundary. - Adrian Englhardt; Holger Trittenbach; Daniel Kottke; Bernhard Sick; Klemens Böhm (Mach. Learn., 2022)

Stream-based active learning for sliding windows under the influence of verification latency. - Tuan Pham; Daniel Kottke; Georg Krempl; Bernhard Sick (Mach. Learn., 2022)

Exploring the Potential of Optimal Active Learning via a Non-myopic Oracle Policy. - Christoph Sandrock; Marek Herde; Daniel Kottke; Bernhard Sick (DS, 2023)

Active Label Refinement for Semantic Segmentation of Satellite Images. - Tuan Pham Minh; Jayan Wijesingha; Daniel Kottke; Marek Herde; Denis Huseljic; Bernhard Sick; Michael Wachendorf; Thomas Esch (CoRR, 2023)