Research Group of Dr. T. Ullrich
Institute for Numerical Simulation
maximize

Dipl.-Math. Sebastian Mayer

Dipl.-Math. Sebastian Mayer
Address: Institut für Numerische Simulation
Endenicher Allee 62
53115 Bonn
Germany
Office: Villa Maria 1.004
Phone: +49 228 73 62243
E-Mail: mayer.ins.uni-bonn.de

Publications

Journal Papers:

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

Thesis:

[1] S. Mayer. Multilevel Rank-1 Lattice Rules for Infinite-dimensional Integration Problems. Technische Universität Darmstadt, 2011. Diploma thesis.
bib | .pdf 1 ]

Other Reports:

[1] S. Mayer. Reconstruction of ridge functions from function values. Oberwolfach Report No. 6, 2015. Extended abstract.
bib | DOI | .pdf 1 ]
[2] S. Mayer. Tractability results for classes of ridge functions. Oberwolfach Report No. 49, 2013. Extended abstract.
bib | DOI | .pdf 1 ]

Presentation slides:

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