Worst-Case Learning from Inaccurate Data and under Multifidelity Models

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• Simon Foucart  (Texas A&M University)
• Monday 15 July 2024, 11:30-12:10
• Seminar Room 1, Newton Institute.

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DREW01 - Multivariate approximation, discretization, and sampling recovery

This talk showcases the speaker’s recent results in the field of Optimal Recovery, viewed as a trustworthy Learning Theory focusing on the worst case. At the core of several results presented here is a scenario, resolved in the global and the local settings, where the model set is the intersection of two hyperellipsoids. This has implications in optimal recovery from deterministically inaccurate data and in optimal recovery under a multifidelity-inspired model. In both situations, the theory becomes richer when considering the optimal estimation of linear functionals. This particular case also comes with additional results in the presence of randomly inaccurate data.

This talk is part of the Isaac Newton Institute Seminar Series series.

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