Any convex L-Lipschitz functional on a compact convex subset of a separable Hilbert space can be uniformly approximated to arbitrary accuracy by an explicit convex L-Lipschitz reconstruction from finitely many linear measurements, exactly implementable by a ReLU-MLP.
arXiv preprint arXiv:2511.01125 , year=
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Adaptivity never hinders uniform approximation of task families but its advantages vary across four scenarios when moving from unrestricted to ReLU-realizable regimes.
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Structure-Preserving Reconstruction of Convex Lipschitz Functionals on Hilbert Spaces from Finite Samples
Any convex L-Lipschitz functional on a compact convex subset of a separable Hilbert space can be uniformly approximated to arbitrary accuracy by an explicit convex L-Lipschitz reconstruction from finitely many linear measurements, exactly implementable by a ReLU-MLP.
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Adaptivity Under Realizability Constraints: Comparing In-Context and Agentic Learning
Adaptivity never hinders uniform approximation of task families but its advantages vary across four scenarios when moving from unrestricted to ReLU-realizable regimes.