Dr. Daniel Kottke

Teamleiter: Methods for Intelligent Interactive Systems (I2S)

Kottke, Daniel
Telefon
+49 561 804-6036
Fax
+49 561 804-6022
E-Mail
Standort
Wilhelmshöher Allee 73
34121 Kassel
Raum
WA-altes Gebäude (WA 73), ohne Raumangabe

Pu­bli­ka­tio­nen

[ 2022 ] [ 2021 ] [ 2020 ] [ 2019 ] [ 2018 ] [ 2017 ] [ 2016 ] [ 2015 ] [ 2014 ]

2022 [ nach oben ]

  • 1.
    Kottke, D., Sandrock, C., Krempl, G., Sick, B.: A Stopping Criterion for Transductive Active Learning. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD). Springer (2022).
     

2021 [ nach oben ]

  • 1.
    Kottke, D., Herde, M., Sandrock, C., Huseljic, D., Krempl, G., Sick, B.: Toward optimal probabilistic active learning using a Bayesian approach. Machine Learning. 110, 1199–1231 (2021).
     
  • 2.
    Pham, T., Kottke, D., Krempl, G., Sick, B.: Stream-Based Active Learning for Sliding Windows Under Verification Latency. Machine Learning. (2021).
     
  • 3.
    Krempl, G., Kottke, D., Pham, T.: Statistical Analysis of Pairwise Connectivity. International Conference on Discovery Science (DS). bll. 138–148. Springer (2021).
     
  • 4.
    Huseljic, D., Sick, B., Herde, M., Kottke, D.: Separation of Aleatoric and Epistemic Uncertainty in Deterministic Deep Neural Networks. International Conference on Pattern Recognition (ICPR). bll. 9172–9179. IEEE (2021).
     
  • 5.
    Herde, M., Kottke, D., Huseljic, D., Sick, B.: Multi-annotator Probabilistic Active Learning. International Conference on Pattern Recognition (ICPR). bll. 10281–10288. IEEE (2021).
     
  • 6.
    Kottke, D.: A Holistic, Decision-Theoretic Framework for Pool-Based Active Learning, (2021).
     

2020 [ nach oben ]

  • 1.
    Kottke, D., Herde, M., Sandrock, C., Huseljic, D., Krempl, G., Sick, B.: Toward Optimal Probabilistic Active Learning Using a Bayesian Approach. arXiv e-prints. arXiv:2006.01732 (2020).
     
  • 2.
    Pham Minh, T., Kottke, D., Tsarenko, A., Gruhl, C., Sick, B.: Improving Self-Adaptation For Multi-Sensor Activity Recognition with Active Learning. International Joint Conference on Neural Networks (IJCNN). IEEE (2020).
     
  • 3.
    Englhardt, A., Trittenbach, H., Kottke, D., Sick, B., Böhm, K.: Efficient SVDD Sampling with Approximation Guarantees for the Decision Boundary. arXiv e-prints. arXiv:2009.13853 (2020).
     

2019 [ nach oben ]

  • 1.
    Kottke, D., Schellinger, J., Huseljic, D., Sick, B.: Limitations of Assessing Active Learning Performance at Runtime. arXiv e-prints. arXiv:1901.10338 (2019).
     
  • 2.
    Sandrock, C., Herde, M., Calma, A., Kottke, D., Sick, B.: Combining Self-reported Confidences from Uncertain Annotators to Improve Label Quality. International Joint Conference on Neural Networks (IJCNN). bll. 1–8. IEEE (2019).
     

2018 [ nach oben ]

  • 1.
    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. International Workshop on Self-optimisation in Organic and Autonomic Computing Systems (SAOS), ARCS. bll. 1–8. VDE (2018).
     
  • 2.
    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 (IJCNN). IEEE, Rio de Janiero, Brazil (2018).
     
  • 3.
    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, 16 (2018).
     
  • 4.
    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 (IJCNN). IEEE, Rio de Janiero, Brazil (2018).
     
  • 5.
    Calma, A., Stolz, M., Kottke, D., Tomforde, S., Sick, B.: Active Learning with Realistic Data -- A Case Study. International Joint Conference on Neural Networks (IJCNN). IEEE, Rio de Janiero, Brazil (2018).
     

2017 [ nach oben ]

  • 1.
    Lang, D., Kottke, D., Krempl, G., Sick, B.: Probabilistic Active Learning with Structure-Sensitive Kernels. Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll. 37–48 (2017).
     
  • 2.
    Beyer, C., Bieshaar, M., Calma, A., Heck, H., Kottke, D., Würtz, R.: Learning Without Ground Truth. In: Tomforde, S. en Sick, B. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2017. kassel university press, Bochum, Germany (2017).
     
  • 3.
    Calma, A., Kottke, D., Sick, B., Tomforde, S.: Learning to Learn: Dynamic Runtime Exploitation of Various Knowledge Sources and Machine Learning Paradigms. Workshop on Self-Improving System Integration (SISSY), FAS*W. bll. 109–116. IEEE, Tucson, AZ (2017).
     
  • 4.
    Würtz, R.P., Tomforde, S., Calma, A., Kottke, D., Sick, B.: Interactive Learning Without Ground Truth. In: Tomforde, S. en Sick, B. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2017. bll. 1–4. kassel university press, Kassel, Germany (2017).
     
  • 5.
    Kottke, D.: Enhanced Probabilistic Active Learning: Cost-sensitive, Unbalanced, Time-variant, Self-optimising, and Parameter-free. In: Tomforde, S. en Sick, B. (reds.) Organic Computing -- Doctoral Dissertation Colloquium 2017. bll. 67–78. kassel university press, Kassel, Germany (2017).
     
  • 6.
    Kottke, D., Calma, A., Huseljic, D., Krempl, G., Sick, B.: Challenges of Reliable, Realistic and Comparable Active Learning Evaluation. Workshop on Interactive Adaptive Learning (IAL), ECML PKDD. bll. 2–14 (2017).
     

2016 [ nach oben ]

  • 1.
    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. Workshop on the Future of Interactive Learning Machines, NIPS. bll. 1–9. , Barcelona, Spain (2016).
     
  • 2.
    Kottke, D., Krempl, G., Lang, D., Teschner, J., Spiliopoulou, M.: Multi-Class Probabilistic Active Learning. European Conference on Artificial Intelligence (ECAI). bll. 586–594. IOS Press (2016).
     
  • 3.
    Lang, D., Kottke, D., Krempl, G., Spiliopoulou, M.: Investigating Exploratory Capabilities of Uncertainty Sampling using SVMs in Active Learning. Workshop on Active Learning: Applications, Foundations and Emerging Trends, iKnow. bll. 25–34. , Graz, Austria (2016).
     
  • 4.
    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, 160092 (2016).
     
  • 5.
    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), iKNOW (2016).
     

2015 [ nach oben ]

  • 1.
    Kottke, D., Krempl, G., Spiliopoulou, M.: Probabilistic Active Learning in Datastreams. Symposium on Intelligent Data Analysis (IDA). bll. 145–157. Springer (2015).
     
  • 2.
    Krempl, G., Kottke, D., Lemaire, V.: Optimised probabilistic active learning (OPAL) For Fast, Non-Myopic, Cost-Sensitive Active Classification. Machine Learning. 100, 449–476 (2015).
     
  • 3.
    Kottke, D., Gulamhussene, G., Tönnies, K.: Data-Driven Spine Detection for Multi-Sequence MRI. Workshop über Bildverarbeitung für die Medizin (BVM). bll. 5–10. Springer (2015).
     

2014 [ nach oben ]

  • 1.
    Krempl, G., Kottke, D., Spiliopoulou, M.: Probabilistic Active Learning: Towards Combining Versatility, Optimality and Efficiency. International Conference on Discovery Science (DS). bll. 168–175. Springer (2014).
     
  • 2.
    Krempl, G., Kottke, D., Spiliopoulou, M.: Probabilistic Active Learning: A Short Proposition. European Conference on Artificial Intelligence (ECAI). IOS Press (2014).