Sebastian Mair

Leuphana University of Lüneburg
Institute of Information Systems
Machine Learning Group
Universitätsallee 1, C4.308a
21335 Lüneburg
Fon +49.4131.677-1664
Fax +49.4131.677-1749

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About Me

I am a PhD student at the Machine Learning Group of Prof. Dr. Ulf Brefeld at the Leuphana University of Lüneburg. I received a Master of Science in Computer Science as well as a Bachelor of Science in Mathematics from Technische Universität Darmstadt and a Bachelor of Science in Computer Science from Hochschule Darmstadt University of Applied Sciences.


  • Advanced Machine Learning (W17)
  • Introduction to Intelligent Data Analysis (S18)
  • Learning from Data (W16)
  • Programming in Python (S16,S17,W17,S18)
  • Statistics for Computer Scientists (W16,W17,W18)
  • Storage and Mining of Massive Datasets (S16,S17,S18)


  • Sebastian Mair, Yannick Rudolph, Vanessa Closius and Ulf Brefeld. Frame-based Optimal Design. Proceedings of the European Conference on Machine Learning, 2018. (to appear) [pdf]
  • Sebastian Mair and Ulf Brefeld. Exploiting the Frame for Active Learning in Multi-class Classification (abstract). ICML Workshop on Geometry in Machine Learning, 2018. [pdf]
  • Ulf Brefeld, Jan Lasek and Sebastian Mair. Probabilistic Movement Models and Zones of Control. Machine Learning Journal, Special Issue on Soccer Analytics, 2018. [link] [pdf]
  • Sebastian Mair and Ulf Brefeld. Distributed Robust Gaussian Process Regression. Knowledge and Information Systems, May 2018, Volume 55, Issue 2, pages 415-435, 2018. [link] [pdf]
  • Sebastian Mair, Ahcène Boubekki and Ulf Brefeld. Frame-based Matrix Factorizations (abstract). LWDA Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML), 2017. [pdf]
  • Sebastian Mair, Ahcène Boubekki and Ulf Brefeld. Frame-based Data Factorizations. Proceedings of the 34th International Conference on Machine Learning, PMLR 70:2305-2313, 2017. [link] [pdf]
  • Marcel Schäfer, Sebastian Mair, Waldemar Berchtold, and Martin Steinebach. Universal threshold calculation for fingerprinting decoders using mixture models. In Proceedings of the 3rd ACM Workshop on Information Hiding and Multimedia Security, pages 109–114. ACM, 2015. [link]

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