Daniel Kottke

Address University of Kassel
Intelligent Embedded Systems
Wilhelmshöher Allee 67
34121 Kassel
Germany
Room 4109
Telephone +49 561 804 6036

[2019] [2018] [2017] [2016] [2015] [2014]

2019 [to top]

  • Kottke, D., Schellinger, J., Huseljic, D., Sick, B.: Limitations of Assessing Active Learning Performance at Runtime. CoRR. abs/1901.10338, (2019).
     
  • Sandrock, C., Herde, M., Calma, A., Kottke, D., Sick, B.: Combining Self-reported Confidences from Uncertain Annotators to Improve Label Quality. 2019 International Joint Conference on Neural Networks (IJCNN). pp. 1-8 (2019).
     

2018 [to top]

  • Scharei, K., Herde, M., Bieshaar, M., Calma, A., Kottke, D., Sick, B.: Automated Active Learning with a Robot. Archives of Data Science, Series A (Online First). 5, A16, 15 S. online (2018).
     
  • Würtz, R.P., Tomforde, S., Calma, A., Kottke, D., Sick, B.: Interactive Learning Without Ground Truth. Organic Computing: Doctoral Dissertation Colloquium 2017. pp. 1-3. kassel university press GmbH (2018).
     
  • Kottke, D.: Enhanced Probabilistic Active Learning: Cost-sensitive, Unbalanced, Time-variant, Self-optimising, and Parameter-free. Organic Computing: Doctoral Dissertation Colloquium 2017. pp. 67-78. kassel university press GmbH (2018).
     
  • Tomforde, S., Dehling, T., Haux, R., Huseljic, D., Kottke, D., Scheerbaum, J., Sick, B., Sunyaev, A., Wolf, K.-H.: Towards Proactive Health-enabling Living Environments : Simulation-based Study and Research Challenges. ARCS Workshop 2018, 31th International Conference on Architecture of Computing Systems. p. 1--8. VDE (2018).
     
  • Herde, M., Kottke, D., Calma, A., Bieshaar, M., Deist, S., Sick, B.: Active Sorting -- An Efficient Training of a Sorting Robot with Active Learning Techniques. International Joint Conference on Neural Networks. , Rio de Janiero, Brazil (2018).
     
  • Kottke, D., Calma, A., Huseljic, D., Sandrock, C., Kachergis, G., Sick, B.: The Other Human in The Loop -- A Pilot Study to Find Selection Strategies for Active Learning. International Joint Conference on Neural Networks. , Rio de Janiero, Brazil (2018).
     
  • Calma, A., Stolz, M., Kottke, D., Tomforde, S., Sick, B.: Active Learning with Realistic Data -- A Case Study. International Joint Conference on Neural Networks. , Rio de Janiero, Brazil (2018).
     

2017 [to top]

  • Lang, D., Kottke, D., Krempl, G., Sick, B.: Probabilistic Active Learning with Structure-Sensitive Kernels. Proceedings of the Workshop and Tutorial on Interactive Adaptive Learning. p. 37--48. CEUR Workshop Proceedings (2017).
     
  • Calma, A., Kottke, D., Sick, B., Tomforde, S.: Learning to Learn: Dynamic Runtime Exploitation of Various Knowledge Sources and Machine Learning Paradigms. SISSY 2017: 4th International Workshop on Self-Improving System Integration. p. 109--116. , Tucson, AZ (2017).
     
  • Kottke, D., Calma, A., Huseljic, D., Krempl, G., Sick, B.: Challenges of Reliable, Realistic and Comparable Active Learning Evaluation. Proceedings of the Workshop and Tutorial on Interactive Adaptive Learning @ ECMLPKDD 2017. pp. 2-14 (2017).
     
  • Beyer, C., Bieshaar, M., Calma, A., Heck, H., Kottke, D., Würtz, R.: Learning Without Ground Truth. Organic Computing -- Doctoral Dissertation Colloquium 2017. , Bochum, Germany (2017).
     

2016 [to top]

  • Lang, D., Kottke, D., Krempl, G., Spiliopoulou, M.: Investigating Exploratory Capabilities of Uncertainty Sampling using SVMs in Active Learning. In: Krempl, G., Lemaire, V., Lughofer, E., and Kottke, D. (eds.) Active Learning: Applications, Foundations and Emerging Trends @iKnow. p. 25--34. , Graz, Austria (2016).
     
  • Kottke, D., Krempl, G., Stecklina, M., Styp von Rekowski, C., Sabsch, T., Pham Minh, T., Deliano, M., Spiliopoulou, M., Sick, B.: Probabilistic Active Learning for Active Class Selection. In: Mathewson, K., Subramanian, K., and Loftin, R. (eds.) NIPS Workshop on the Future of Interactive Learning Machines. p. 1--9. , Barcelona, Spain (2016).
     
  • Matuszyk, P., Castillo, R.T., Kottke, D., Spiliopoulou, M.: A Comparative Study on Hyperparameter Optimization for Recommender Systems. Workshop on Recommender Systems and Big Data Analytics (RS-BDA'16) @ iKNOW 2016 (2016).
     
  • Kottke, D., Krempl, G., Lang, D., Teschner, J., Spiliopoulou, M.: Multi-Class Probabilistic Active Learning. ECAI. pp. 586-594. IOS Press (2016).
     
  • Hanke, M., Adelhöfer, N., Kottke, D., Iacovella, V., Sengupta, A., Kaule, F.R., Nigbur, R., Waite, A.Q., Baumgartner, F., Stadler, J.: A studyforrest extension, simultaneous fMRI and eye gaze recordings during prolonged natural stimulation. Scientific Data. 3, (2016).
     

2015 [to top]

  • Kottke, D., Gulamhussene, G., Tönnies, K.: Data-Driven Spine Detection for Multi-Sequence MRI. Bildverarbeitung für die Medizin (BVM2015). pp. 5-10. Springer Berlin Heidelberg (2015).
     
  • Krempl, G., Kottke, D., Lemaire, V.: Optimised probabilistic active learning (OPAL) For Fast, Non-Myopic, Cost-Sensitive Active Classification. Machine Learning. 1-28 (2015).
     
  • Kottke, D., Krempl, G., Spiliopoulou, M.: Probabilistic Active Learning in Datastreams. Advances in Intelligent Data Analysis XIV. pp. 145-157. Springer International Publishing (2015).
     

2014 [to top]

  • Krempl, G., Kottke, D., Spiliopoulou, M.: Probabilistic Active Learning: Towards Combining Versatility, Optimality and Efficiency. Proceedings of the 17th Int. Conf. on Discovery Science (DS), Bled. Springer (2014).
     
  • Krempl, G., Kottke, D., Spiliopoulou, M.: Probabilistic Active Learning: A Short Proposition. Proceedings of the 21st European Conference on Artificial Intelligence (ECAI2014), August 18 -- 22, 2014, Prague, Czech Republic. IOS Press (2014).