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Staff Dr. rer. nat. Sebastian Mayer

Mr. Mayer has left the institute. This page is no longer maintained.

Contact Information

E-Mail: ed tod refohnuarf tod iacs ta reyam tod naitsabesa tod b@foo tod de

I am a postdoctoral researcher in the department for numerical data-driven prediction at Fraunhofer SCAI.

My current research focuses on machine learning and biologically inspired algorithms for cyber-physical systems. Before I have worked on complexity-theoretical aspects of high-dimensional approximation problems.

Current Research Projects

Publications

Preprints

  1. The recovery of ridge functions on the hypercube suffers from the curse of dimensionality. B. Doerr and S. Mayer. arXiv e-prints, Mar 2019. BibTeX arXiv
  2. Entropy numbers of finite dimensional mixed-norm balls and function space embeddings with small mixed smoothness. S. Mayer and T. Ullrich. arXiv e-prints, Apr 2019. BibTeX arXiv

Articles

  1. Counting via entropy: new preasymptotics for the approximation numbers of Sobolev embeddings. T. Kühn, S. Mayer, and T. Ullrich. SIAM Journ. on Numerical Analysis, 54(6):3625 – 3647, 2016. BibTeX PDF arXiv
  2. Entropy numbers of spheres in Banach and quasi-Banach spaces. A. Hinrichs and S. Mayer. J. Approx. Theory, 200:144–152, 2015. BibTeX arXiv
  3. Entropy and sampling numbers of classes of ridge functions. S. Mayer, T. Ullrich, and J. Vybiral. Constructive Approximation, 42:231–264, 2015. BibTeX PDF arXiv
  4. On Weighted Hilbert Spaces and Integration of Functions of Infinitely Many Variables. M. Gnewuch, Sebastian, Mayer, and K. Ritter. J. Complexity, 30(2):29–47, 2014. BibTeX PDF

Reports

  1. Reconstruction of ridge functions from function values. S. Mayer. Oberwolfach Report No. 6, 2015. Extended abstract. BibTeX PDF
  2. Tractability results for classes of ridge functions. S. Mayer. Oberwolfach Report No. 49, 2013. Extended abstract. BibTeX PDF

Theses

  1. Preasymptotic error bounds via metric entropy. S. Mayer. Verlag Dr. Hut, München, 2018. PhD thesis. BibTeX
  2. Multilevel Rank-1 Lattice Rules for Infinite-dimensional Integration Problems. S. Mayer. Technische Universität Darmstadt, 2011. Diploma thesis. BibTeX PDF

Talks

  1. On random projections in machine learning. S. Mayer. Helmholtz ICB Seminar, Munich, 2015. BibTeX PDF
  2. The effect of sparsity and relaxations thereof in certain function approximation problems. S. Mayer. Guest lecture, JKU Linz, 2015. BibTeX PDF
  3. Approximation of ridge functions: tractability results. S. Mayer. MCQMC Leuven, 2014. BibTeX PDF
  4. On Infinite-dimensional Integration in Weighted Hilbert Spaces. S. Mayer. HDA Canberra, 2013. BibTeX PDF
  5. Randomized dimensionality reduction in machine learning. S. Mayer. CSA Berlin, 2013. Poster. BibTeX PDF