Dr. ing. G.M. (Georg) Krempl
Assistant Professor
Publications
2022
Scholarly publications
Pham, T., Kottke, D., Krempl, G., & Sick, B. (2022). Stream-based active learning for sliding windows under the influence of verification latency. Machine Learning, 111(6), 2011–2036. https://doi.org/10.1007/s10994-021-06099-z
2021
Scholarly publications
Krempl, G., Kottke, D., & Minh, T. P. (2021). ACE - A Novel Approach for the Statistical Analysis of Pairwise Connectivity. (pp. 1-15). arXiv. https://doi.org/10.48550/arXiv.2108.04289
Krempl, G., Kottke, D., & Pham, T. (2021). Statistical Analysis of Pairwise Connectivity. In C. Soares, & L. Torgo (Eds.), Discovery Science: 24th International Conference, DS 2021, Halifax, NS, Canada, October 11–13, 2021, Proceedings (pp. 138-148). (Lecture Notes in Computer Science; Vol. 12986). Springer. https://doi.org/10.1007/978-3-030-88942-5_11
Kottke, D., Krempl, G., Stecklina, M., Rekowski, C. S. V., Sabsch, T., Minh, T. P., Deliano, M., Spiliopoulou, M., & Sick, B. (2021). Probabilistic Active Learning for Active Class Selection. (pp. 1-9). arXiv. https://doi.org/10.48550/arXiv.2108.03891
Kok, T. T., Krempl, G., & Schnack, H. G. (2021). Implementation of and experimental software for active selection of classification features. Software Impacts, 9, 1-3. [100103]. https://doi.org/10.1016/J.SIMPA.2021.100103
Kottke, D., Herde, M., Sandrock, C., Huseljic, D., Krempl, G., & Sick, B. (2021). Toward optimal probabilistic active learning using a Bayesian approach. Machine Learning, 110(6), 1199-1231. https://doi.org/10.1007/S10994-021-05986-9
2020
Scholarly publications
Krempl, G., Hofer, V., Webb, G. I., & Hüllermeier, E. (2020). Beyond Adaptation - Understanding Distributional Changes (Dagstuhl Seminar 20372). https://doi.org/10.4230/DAGREP.10.4.1
Kottke, D., Krempl, G., Lemaire, V., Holzinger, A., & Calma, A. (2020). Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2020), Ghent, Belgium, September 14th, 2020. https://dblp.org/rec/conf/pkdd/2020ial
Berthold, M., Feelders, A. J., & Krempl, G. M. (Eds.) (2020). Advances in Intelligent Data Analysis XVIII. (LNCS), (Information Systems and Applications, incl. Internet/Web, and HCI ; Vol. 12080). SPRING. https://doi.org/10.1007/978-3-030-44584-3
Kottke, D. (Ed.), Krempl, G. M., Lemaire, V., Holzinger, A., & Calma, A. (2020). Proceedings of the Workshop on Interactive Adaptive Learning (IAL 2020). CEUR Workshop Proceedings.
https://dspace.library.uu.nl/bitstream/handle/1874/415338/ialatecml2020.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/415338/ialatecml2020.pdf?sequence=1
Niemeijer, K., Feskens, R., Krempl, G., Koops, J., & Brinkhuis, M. J. S. (2020). Constructing and Predicting School Advice for Academic Achievement: A Comparison of Item Response Theory and Machine Learning Techniques. In Proceedings of the 10th International Conference on Learning Analytics and Knowledge (LAK ’20) (pp. 462-471). [15] ACM. https://doi.org/10.1145/3375462.3375486
2019
Scholarly publications
Krempl, G. M., Hofer, V., Kottke, D., & Lang, D. (2019). Machine Learning with Limited Supervision. Poster session presented at ICT.Open, Hilversum, Netherlands.
Krempl, G. M., Lang, D., Hofer, V., & Bijak, K. (2019). Towards Understanding and Predicting Distributional Changes. Abstract from Credit Scoring and Credit Control, Edinburgh, United Kingdom.
Kottke, D. (Ed.), Krempl, G. M. (Ed.), Lemaire, V., Holzinger, A., & Calma, A. (2019). Proceedings of the Workshop on Interactive Adaptive Learning 2019. CEUR Workshop Proceedings. http://ceur-ws.org/Vol-2444/
Krempl, G. M., Lang, D., & Hofer, V. (2019). Temporal density extrapolation using a dynamic basis approach. Data Mining and Knowledge Discovery, 33, 1324-1356. https://doi.org/10.1007/s10618-019-00636-0
2018
Scholarly publications
Krempl, G. M., Lemaire, V., Kottke, D., Calma, A., Holzinger, A., Polikar, R., & Sick, B. (Eds.) (2018). Interactive Adaptive Learning: Proceedings of the Workshop on Interactive Adaptive Learning co-located with European Conference on Machine Learning (ECML 2018) and Principles and Practice of Knowledge Discovery in Databases (PKDD 2018) Dublin, Ireland, September 10th, 2018. CEUR Workshop Proceedings. http://ceur-ws.org/Vol-2192/
2017
Scholarly publications
Krempl, G., Lemaire, V., Polikar, R., Sick, B., Kottke, D., & Calma, A. (2017). Proceedings of the Workshop and Tutorial on Interactive Adaptive Learning co-located with European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2017), Skopje, Macedonia, September 18, 2017. https://dblp.org/rec/conf/pkdd/2017ial
Kottke, D., Calma, A., Huseljic, D., Krempl, G. M., & Sick, B. (2017). Challenges of Reliable, Realistic and Comparable Active Learning Evaluation. In Proceedings of the Workshop and Tutorial on Interactive Adaptive Learning (pp. 2-14)
https://dspace.library.uu.nl/bitstream/handle/1874/359528/ialatecml_paper0.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/359528/ialatecml_paper0.pdf?sequence=1
Lang, D., Kottke, D., & Krempl, G. M. (2017). Probabilistic Active Learning with Structure-Sensitive Kernels. In Proceedings of the Workshop and Tutorial on Interactive Adaptive Learning (Vol. 1924, pp. 37-48). CEUR Workshop Proceedings.
https://dspace.library.uu.nl/bitstream/handle/1874/359527/ialatecml_paper3.pdf?sequence=1
https://dspace.library.uu.nl/bitstream/handle/1874/359527/ialatecml_paper3.pdf?sequence=1
2016
Scholarly publications
Bergner, B., & Krempl, G. (2016). Active Subtopic Detection in Multitopic Data.. 35-44. https://dblp.org/rec/conf/iknow/BergnerK16
Krempl, G., Lemaire, V., Lughofer, E., & Kottke, D. (2016). Proceedings of the Workshop on Active Learning - Applications, Foundations and Emerging Trends co-located with International Conference on Knowledge Technologies and Data-driven Business (i-KNOW 2016), Graz, Austria, October 18, 2016. https://dblp.org/rec/conf/iknow/2016al
Lang, D., Kottke, D., Krempl, G., & Spiliopoulou, M. (2016). Investigating Exploratory Capabilities of Uncertainty Sampling using SVMs in Active Learning.. 25-34. https://dblp.org/rec/conf/iknow/LangKKS16
Kottke, D., Krempl, G., Lang, D., Teschner, J., & Spiliopoulou, M. (2016). Multi-Class Probabilistic Active Learning.. 586-594. https://doi.org/10.3233/978-1-61499-672-9-586
2015
Scholarly publications
Beyer, C., Krempl, G., & Lemaire, V. (2015). How to select information that matters - a comparative study on active learning strategies for classification.. 2:1-2:8. https://doi.org/10.1145/2809563.2809594
Krempl, G., Ha, T. C., & Spiliopoulou, M. (2015). Clustering-Based Optimised Probabilistic Active Learning (COPAL).. 101-115. https://doi.org/10.1007/978-3-319-24282-8_10
Krempl, G., Kottke, D., & Lemaire, V. (2015). Optimised probabilistic active learning (OPAL) - For fast, non-myopic, cost-sensitive active classification. Machine Learning, 100(2-3), 449-476. https://doi.org/10.1007/S10994-015-5504-1
Kottke, D., Krempl, G., & Spiliopoulou, M. (2015). Probabilistic Active Learning in Datastreams.. 145-157. https://doi.org/10.1007/978-3-319-24465-5_13
Siddiqui, Z. F., Krempl, G., Spiliopoulou, M., Peña, J. M., Paul, N., & Maestú, F. (2015). Predicting the post-treatment recovery of patients suffering from traumatic brain injury (TBI). Brain Informatics, 2(1), 33-44. https://doi.org/10.1007/S40708-015-0010-6
2014
Scholarly publications
Krempl, G., Kottke, D., & Spiliopoulou, M. (2014). Probabilistic Active Learning - A Short Proposition.. 1049-1050. https://doi.org/10.3233/978-1-61499-419-0-1049
Krempl, G., Kottke, D., & Spiliopoulou, M. (2014). Probabilistic Active Learning - Towards Combining Versatility, Optimality and Efficiency.. 168-179. https://doi.org/10.1007/978-3-319-11812-3_15
Siddiqui, Z. F., Krempl, G., Spiliopoulou, M., Peña, J. M., Paul, N., & Maestú, F. (2014). Are Some Brain Injury Patients Improving More Than Others?. 376-387. https://doi.org/10.1007/978-3-319-09891-3_35
Krempl, G., Zliobaite, I., Brzezinski, D., Hüllermeier, E., Last, M., Lemaire, V., Noack, T., Shaker, A., Sievi, S., Spiliopoulou, M., & Stefanowski, J. (2014). Open challenges for data stream mining research. SIGKDD Explorations , 16(1), 1-10. https://doi.org/10.1145/2674026.2674028
2013
Scholarly publications
Matuszyk, P., Krempl, G., & Spiliopoulou, M. (2013). Correcting the Usage of the Hoeffding Inequality in Stream Mining.. 298-309. https://doi.org/10.1007/978-3-642-41398-8_26
Hofer, V., & Krempl, G. (2013). Drift mining in data - A framework for addressing drift in classification. Computational Statistics and Data Analysis, 57(1), 377-391. https://doi.org/10.1016/J.CSDA.2012.07.007
2012
Scholarly publications
Hofer, V., & Krempl, G. (2012). A hierarchical tree layout algorithm with an application to corporate management in a change process. Expert Systems with Applications: X, 39(15), 12123-12130. https://doi.org/10.1016/J.ESWA.2012.04.002
2011
Scholarly publications
Krempl, G., Siddiqui, Z. F., & Spiliopoulou, M. (2011). Online Clustering of High-Dimensional Trajectories under Concept Drift.. 261-276. https://doi.org/10.1007/978-3-642-23783-6_17
Krempl, G., & Hofer, V. (2011). Drift Models and Classification in Presence of Latency and Drift.. 65-72. https://dblp.org/rec/conf/lwa/KremplH11
Krempl, G. (2011). The Algorithm APT to Classify in Concurrence of Latency and Drift.. 222-233. https://doi.org/10.1007/978-3-642-24800-9_22
Krempl, G., & Hofer, V. (2011). Classification in Presence of Drift and Latency.. 596-603. https://doi.org/10.1109/ICDMW.2011.47
2009
Scholarly publications
Krempl, G., & Hofer, V. (2009). Partitioner Trees for Classification - A New Ensemble Method. In O. Okun, & G. Valentini (Eds.), Applications of Supervised and Unsupervised Ensemble Methods (Vol. 245, pp. 93-112). (Studies in Computational Intelligence). Springer Heidelberg. https://doi.org/10.1007/978-3-642-03999-7_6