Out-of-distribution (OOD) generalization is a central challenge in scientific machine learning. We study regression problems in which the test distribution differs from the training distribution and ask: under what assumptions on the target function or operator is stable extrapolation possible, and how far beyond the t...
Ben Adcock, Simone Brugiapaglia, Xue-Meng Wang· 0 citations
Out-of-distribution (OOD) generalization is a central challenge in scientific machine learning. We study regression problems in which the test distribution differs from the training distribution and ask: under what assumptions on the target function or operator is stable extrapolation possible, and how far beyond the t...
Ben Adcock, Simone Brugiapaglia, Xue-Meng Wang· 0 citations
By tightly characterizing the sample complexity, this work confirms the intrinsic difficulty of learning Lipschitz operators, regardless of the data or learning technique.
Ben Adcock, Michael Griebel, Gregor Maier· 12 citations
A key question in operator learning is how to design surrogate operators with provable approximation guarantees in reasonable computational time. Whereas smooth operators can be approximated efficiently, i.e., with at least algebraic convergence in the amount of training data, learning finitely regular operators is kno...
Ben Adcock, Michael Griebel, Gregor Maier· 0 citations
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