Universal nonadaptive algorithms recover anisotropic Sobolev functions near-optimally via compressed sensing on Fourier coefficients, while linear methods suffer dimension-dependent polylog penalties.
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Sparse polynomial surrogates approximate parametric diffusion on community-structured graphs, with convergence guarantees via holomorphic regularity and tests on synthetic and real graphs.
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Universal, sample-optimal algorithms for recovery of anisotropic functions from i.i.d. samples
Universal nonadaptive algorithms recover anisotropic Sobolev functions near-optimally via compressed sensing on Fourier coefficients, while linear methods suffer dimension-dependent polylog penalties.
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Surrogate models for diffusion on graphs via sparse polynomials
Sparse polynomial surrogates approximate parametric diffusion on community-structured graphs, with convergence guarantees via holomorphic regularity and tests on synthetic and real graphs.