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Sebastian Mair

Leuphana University of Lüneburg
Universitätsallee 1
21335 Lüneburg, Germany

Office: C4.308a
+49 4131 677 1664
sebastian.mairREMOVE@REMOVEleuphana.de

Google Scholar / Semantic Scholar / dblp / ORCID / github

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.

Research Interests

I am interested in unsupervised learning, representation learning, representative subsets, (geometric) data summarization, generative modeling, probabilistic modeling, and statistical machine learning.

Teaching

  • Advanced Machine Learning (W17)
  • Deep Learning (S21)
  • Intelligent Data Analysis (S20)
  • Introduction to Intelligent Data Analysis (S18,S19)
  • Learning from Data (W16)
  • Machine Learning and Data Mining (W19,W20)
  • Programming in Python (S16,S17,W17,S18)
  • Statistics for Computer Scientists (W16,W17,W18,W19,W20)
  • Storage and Mining of Massive Datasets (S16,S17,S18,S19,S20)

Publications

  • S. G. Fadel, S. Mair, R. da Silva Torres, and U. Brefeld. Contextual Movement Models based on Normalizing Flows. AStA Advances in Statistical Analysis, Special Issue on Statistics in Sports, 2021. [link]
  • S. G. Fadel, S. Mair, R. da Silva Torres, and U. Brefeld. Principled Interpolation in Normalizing Flows. Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 2021. [link] [arXiv] [video]
  • U. Brefeld, J. Lasek, S. Mair. Analyzing Positional Data. In C. Ley, Y. Dominicy (eds.), Science Meets Sports - When Statistics Are More Than Numbers, Cambridge Scholars Publishing, pp 81-94, 2020. [link]
  • S. Mair, S. G. Fadel, R. da Silva Torres, and U. Brefeld. Efficient Normalizing Flows to Polytopes (abstract). Proceedings of the Northern Lights Deep Learning Workshop, 2020.
  • S. G. Fadel, S. Mair, R. da Silva Torres, and U. Brefeld. An Appropriate Prior Distribution for Interpolating Latent Samples in Flow-based Generative Models (abstract). Proceedings of the Northern Lights Deep Learning Workshop, 2020.
  • S. Mair and U. Brefeld. Coresets for Archetypal Analysis. Advances in Neural Information Processing Systems, 2019. [link] [supplement] [code] [poster]
  • M. Tavakol, S. Mair and K. Morik. HyperUCB: Hyperparameter Optimization using Contextual Bandits. ECML-PKDD Workshop on Automating Data Science, 2019. [link]
  • S. Mair, Y. Rudolph, V. Closius and U. Brefeld. Frame-based Optimal Design. Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD), 2018. [link]
  • S. Mair and U. Brefeld. Exploiting the Frame for Active Learning in Multi-class Classification (abstract). ICML Workshop on Geometry in Machine Learning, 2018.
  • U. Brefeld, J. Lasek and S. Mair. Probabilistic Movement Models and Zones of Control. Machine Learning Journal, Special Issue on Soccer Analytics, 2018. [link]
  • S. Mair and U. Brefeld. Distributed Robust Gaussian Process Regression. Knowledge and Information Systems, May 2018, Volume 55, Issue 2, pages 415-435, 2018. [link]
  • S. Mair, A. Boubekki and U. Brefeld. Frame-based Matrix Factorizations (abstract). LWDA Workshop on Knowledge Discovery, Data Mining and Machine Learning (KDML), 2017.
  • S. Mair, A. Boubekki and U. Brefeld. Frame-based Data Factorizations. Proceedings of the 34th International Conference on Machine Learning, PMLR 70:2305-2313, 2017. [link]
  • M. Schäfer, S. Mair, W. Berchtold, and M. 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]