Constructs an efficient mixed test for linear functional testing in sparse regression and proves information-theoretic and low-degree lower bounds on adaptive separation rates for general loadings, with computational hardness evidence via sparse CCA reduction.
23, American Mathematical Soc
2 Pith papers cite this work. Polarity classification is still indexing.
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2026 2verdicts
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For logarithmic weights the spaces match Pilipović spaces and Hermite coefficients decay at explicit rates that imply decay for harmonic-oscillator solutions; for other weights the Hermite projections decay exponentially, and a partial improvement is obtained on Vemuri's subcritical Hardy conjecture
citing papers explorer
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Linear Functional Testing with General Loadings in Sparse Regression: Separation Rates and Computational Barriers
Constructs an efficient mixed test for linear functional testing in sparse regression and proves information-theoretic and low-degree lower bounds on adaptive separation rates for general loadings, with computational hardness evidence via sparse CCA reduction.
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Hermite expansions of functions from the weighted Hardy class
For logarithmic weights the spaces match Pilipović spaces and Hermite coefficients decay at explicit rates that imply decay for harmonic-oscillator solutions; for other weights the Hermite projections decay exponentially, and a partial improvement is obtained on Vemuri's subcritical Hardy conjecture