Partitioner Trees for Classification: A New Ensemble Method. - Georg Krempl; Vera Hofer (Applications of Supervised and Unsupervised Ensemble Methods, 2009)

Classification in Presence of Drift and Latency. - Georg Krempl; Vera Hofer (ICDM Workshops, 2011)

The Algorithm APT to Classify in Concurrence of Latency and Drift. - Georg Krempl (IDA, 2011)

Drift Models and Classification in Presence of Latency and Drift. - Georg Krempl; Vera Hofer (LWA, 2011)

Online Clustering of High-Dimensional Trajectories under Concept Drift. - Georg Krempl; Zaigham Faraz Siddiqui; Myra Spiliopoulou (ECML/PKDD (2), 2011)

A hierarchical tree layout algorithm with an application to corporate management in a change process. - Vera Hofer; Georg Krempl (Expert Syst. Appl., 2012)

Correcting the Usage of the Hoeffding Inequality in Stream Mining. - Pawel Matuszyk; Georg Krempl; Myra Spiliopoulou (IDA, 2013)

Drift mining in data: A framework for addressing drift in classification. - Vera Hofer; Georg Krempl (Comput. Stat. Data Anal., 2013)

Are Some Brain Injury Patients Improving More Than Others? - Zaigham Faraz Siddiqui; Georg Krempl; Myra Spiliopoulou; José M. Peña 002; Nuria Paul; Fernando Maestú (Brain Informatics and Health, 2014)

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)

Open challenges for data stream mining research. - Georg Krempl; Indre Zliobaite; Dariusz Brzezinski; Eyke Hüllermeier; Mark Last; Vincent Lemaire 001; Tino Noack; Ammar Shaker; Sonja Sievi; Myra Spiliopoulou; Jerzy Stefanowski (SIGKDD Explor., 2014)

Clustering-Based Optimised Probabilistic Active Learning (COPAL). - Georg Krempl; Tuan Cuong Ha; Myra Spiliopoulou (Discovery Science, 2015)

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

How to select information that matters: a comparative study on active learning strategies for classification. - Christian Beyer; Georg Krempl; Vincent Lemaire 001 (I-KNOW, 2015)

Temporal Density Extrapolation. - Georg Krempl (AALTD@PKDD/ECML, 2015)

Predicting the post-treatment recovery of patients suffering from traumatic brain injury (TBI). - Zaigham Faraz Siddiqui; Georg Krempl; Myra Spiliopoulou; José M. Peña 002; Nuria Paul; Fernando Maestú (Brain Informatics, 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)

Active Subtopic Detection in Multitopic Data. - Benjamin Bergner; Georg Krempl (AL@iKNOW, 2016)

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

Aktives maschinelles Lernen bei unvollständiger Information - Georg Krempl (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)

Temporal density extrapolation using a dynamic basis approach. - Georg Krempl; Dominik Lang; Vera Hofer (Data Min. Knowl. Discov., 2019)

Constructing and predicting school advice for academic achievement: a comparison of item response theory and machine learning techniques. - Koen Niemeijer; Remco Feskens; Georg Krempl; Jesse Koops; Matthieu J. S. Brinkhuis (LAK, 2020)

Beyond Adaptation: Understanding Distributional Changes (Dagstuhl Seminar 20372). - Georg Krempl; Vera Hofer; Geoffrey I. Webb; Eyke Hüllermeier (Dagstuhl Reports, 2020)

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

Active Selection of Classification Features. - Thomas T. Kok; Rachel M. Brouwer; René C. W. Mandl; Hugo G. Schnack; Georg Krempl (IDA, 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)

Implementation of and experimental software for active selection of classification features. - Thomas T. Kok; Georg Krempl; Hugo G. Schnack (Softw. Impacts, 2021)

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

Tutorial: Interactive Adaptive Learning. - Mirko Bunse; Georg Krempl; Alaa Tharwat; Amal Saadallah (IAL@PKDD/ECML, 2023)

Efficient and General Text Classification: An Active Learning Approach Using Active Learning and NLP to Aid Processes Such as Journalistic Investigations And document Analysis. - Micha van Grinsven; Matthieu J. S. Brinkhuis; Georg Krempl; Joop Snijder (PKDD/ECML Workshops (2), 2023)

Identifying Predictions That Influence the Future: Detecting Performative Concept Drift in Data Streams. - Brandon Gower-Winter; Georg Krempl; Sergey Dragomiretskiy; Tineke Jelsma; Arno Siebes (AAAI, 2025)

Performative Drift Resistant Classification Using Generative Domain Adversarial Networks. - Maciej Makowski; Brandon Gower-Winter; Georg Krempl (IDA, 2025)