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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Exploiting i.i.d. sampling randomness yields a bound on discrete L2 truncation error that follows continuous L2-norm decay, enabling significantly smaller truncation sets than prior L∞-based approaches for weighted Wiener and anisotropic Sobolev spaces.
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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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The devil in the (de)tails: an improved recovery guarantee for sparse approximation
Exploiting i.i.d. sampling randomness yields a bound on discrete L2 truncation error that follows continuous L2-norm decay, enabling significantly smaller truncation sets than prior L∞-based approaches for weighted Wiener and anisotropic Sobolev spaces.