Subspace Uncertainty and Sharp Sampling Thresholds on the Boolean Cube

Abstract: We study Gaussian regression under squared population $L_2$ loss in a known $m$-dimensional subspace of degree-at-most-$k$ functions on the $d$-dimensional Boolean cube. Random inputs can undersample regions essential for prediction, delaying the parametric rate even when the model is known. For fixed $q_0&lt;1/2$, $1\le k\le q_0d$, and sufficiently large fixed $A$, the worst-subspace sample threshold for minimax error $Aσ<sup>2(m+t)/n$ with confidence $1-e<sup>{-t}$, $t\ge\log4$, is [ N=(m+t)\exp{E_{d,k}+O(k^{1/3})}, \quad E_{d,k}=dΨ(k/d), ] where $Ψ(q)=\log2-\mathsf H(\tfrac12-\sqrt{q(1-q)})$ and $\mathsf H$ is binary entropy with natural logarithms. The upper bound holds for every feasible $m$; the matching lower bound holds when $m\le\binom d{\lfloor k<sup>{1/3}\rfloor}$ or $t\ge m$. We sharpen the Polyanskiy--Samorodnitsky uncertainty principle in two respects. First, for fixed leakage $ρ\in(0,1)$, the smallest set carrying a fraction $1-ρ$ of a nonzero degree-at-most-$k$ polynomial's energy has probability $\exp{-E_{d,k}+O_{ρ,q_0}(k<sup>{1/3})}$. An Airy-kernel construction proves that the remainder cannot be $o(k<sup>{1/3})$ in general. Second, we construct a subspace of dimension $\binom d{\lfloor k<sup>{1/3}\rfloor}$ such that every function in the subspace has at least a fraction $1-ρ$ of its energy on the same set, whose probability is at most $\exp{-E_{d,k}+C_{ρ,q_0}k<sup>{1/3}}$. For sufficiently large $k$, this set is a Hamming ball. A striking consequence is an exponential cost of noise: the parametric rate can require $(m+t)4<sup>k\exp{-O(k<sup>{1/3})}$ samples, whereas $O((m+t)2<sup>k)$ suffice for noiseless identification. As $k\to\infty$ with $k/d\to0$, the noisy threshold is $(m+t)\exp{2k+o(k)}$.