13.01 14:30 - 15:30
USI East Campus, Room D0.02 |
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Abstract: I consider the approximation of functions based on function evaluations. This is a well-studied problem in optimal recovery, machine learning, and numerical analysis in general, but many fundamental insights were obtained only recently.
I will discuss some of these insights, including the optimality of least squares in a worst-case setting and corresponding (random) sampling strategies.
I will also present a semi-constructive algorithm that is provably superior to sparse grid interpolation for L2-approximation in 'tensor product spaces'.
Host: Prof. Michael Multerer | |
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Johannes Kepler University Linz | |
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| | Mario Ullrich is a mathematician who works in theoretical numerical analysis and complexity theory, with an emphasis on high-dimensional approximation.
Mario did his PhD in mathematics at the Friedrich Schiller University of Jena and Università Roma Tre, and his habilitation at Johannes Kepler University Linz, where he is a senior scientist.
He is the recipient of the "Joseph F. Traub Prize for Achievement in Information-based Complexity" and the author of the upcoming Acta Numerica article "Approximation of functions: optimal sampling and complexity".
14:30 |
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