Sebastian Mair

Research Associate @ Leuphana University

Leuphana University

Office C4.308a

LĂĽneburg, Germany

about me

I am a PhD student at the machine learning group of Ulf Brefeld at the Leuphana University of LĂĽneburg.

Prior to my PhD studies, 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.

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). 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). 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]