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- W4293251905 abstract "We consider the problem of approximating a function in a general nonlinear subset of $L^2$, when only a weighted Monte Carlo estimate of the $L^2$-norm can be computed. Of particular interest in this setting is the concept of sample complexity, the number of sample points that are necessary to achieve a prescribed error with high probability. Reasonable worst-case bounds for this quantity exist only for particular model classes, like linear spaces or sets of sparse vectors. For more general sets, like tensor networks or neural networks, the currently existing bounds are very pessimistic. By restricting the model class to a neighbourhood of the best approximation, we can derive improved worst-case bounds for the sample complexity. When the considered neighbourhood is a manifold with positive local reach, its sample complexity can be estimated by means of the sample complexities of the tangent and normal spaces and the manifold's curvature." @default.
- W4293251905 created "2022-08-27" @default.
- W4293251905 creator A5057535604 @default.
- W4293251905 date "2022-08-23" @default.
- W4293251905 modified "2023-09-28" @default.
- W4293251905 title "Convergence bounds for local least squares approximation" @default.
- W4293251905 doi "https://doi.org/10.48550/arxiv.2208.10954" @default.
- W4293251905 hasPublicationYear "2022" @default.
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