Zero-SNR Analyticity of the Scalar MMSE Is Equivalent to Gaussianity
- URL: http://arxiv.org/abs/2609.15048v1
- Date: Mon, 14 Sep 2026 05:07:20 GMT
- Title: Zero-SNR Analyticity of the Scalar MMSE Is Equivalent to Gaussianity
- Abstract summary: We prove that the minimum mean-square error $operatornamemmse_X(s)$ is analytic at zero signal-to-noise ratio if and only if $X$ is Gaussian.<n>A zero of $mgeq 2$ splits according to the roots of a Hermitemath and instead contributes a prefactor $n-m/2er_msqrt2n$.
- Score: 0.7499255435598924
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Let $Y_s=\sqrt{s}X+Z$, where $Z$ is standard Gaussian and independent of the real random variable $X$. We prove that, under the square-exponential moment condition $\mathbb{E}e^{βX^2}<\infty$ for some $β>0$, the scalar minimum mean-square error $\operatorname{mmse}_X(s)$ is analytic at zero signal-to-noise ratio if and only if $X$ is Gaussian, with constant random variables included as degenerate Gaussians. The proof converts estimation in the Gaussian channel into a backward heat flow acting on the moment-generating function $M(z)=\mathbb{E}e^{zX}$. Under the stated tail condition, every non-Gaussian input forces $M$ to have a nonzero complex zero. We show that each zero cluster produces a finite singularity in its localized Borel transform at the action $ξ=z_0^2/2$. After removing the action scale, the Borel coefficients have a nonzero $n^{-1/2}$ prefactor for a simple zero. A zero of multiplicity $m\geq 2$ splits according to the roots of a Hermite polynomial and instead contributes a prefactor $n^{-m/2}e^{r_m\sqrt{2n}}$. A finite-disc localization and relative-cycle continuation argument then show that at least one such singularity survives in the full Borel transform. Thus, for every non-Gaussian input in the stated class, the formal zero-SNR expansion is Gevrey-1 but divergent. Rational-MMSE rigidity and the analogous analyticity criterion for mutual information follow as corollaries.
Related papers
- Exact Moments of Gaussian Gram Hafnians Reveal an $n^2/\log n$ Threshold for Weak Anticoncentration [2.8224889996383395]
Anticoncentration is central to hardness arguments for approximate sampling.<n> finite dimensional Haar moment transfer and high probability small ball anticoncentration remain separate problems.
arXiv Detail & Related papers (2026-08-17T19:12:22Z) - High Minima of Gaussian Processes: Overshoots and Minimizer Locations [0.0]
We show that, conditionally on $M>u$, the scaled overshoot $u(M-u)$ converges, as $utoinfty$, to an exponential random variable with mean $_*2$.<n>The results are illustrated by stationary Gaussian processes, fractional Brownian motion, and fractional Brownian sheet.
arXiv Detail & Related papers (2026-07-22T20:31:36Z) - Module Lattice Security (Part III): Structured CVP Distance on the Log-Unit Lattice [1.0152838128195467]
We show that the $L2$ CVP distance from a random short ring element to the log-unit lattice converges to $frac2sqrt6sqrtn$ as $n=2k-1toinfty$.<n>For the $Linfty$ norm, the maximum over $n$ sub-Gaussian coordinates yields $O(sqrtlog n)$ which translates into a sub-polynomial approximation factor for the Short Generator Problem.
arXiv Detail & Related papers (2026-05-17T12:00:59Z) - Rényi exponent landscape of multipartite entanglement in free-fermion systems [51.56484100374058]
We show that the Rényi tripartite information $I_3() exhibits a qualitatively $exclusion-dependent scaling at small Fermi momentum.<n>A direct consequence is a replica obstruction: $I_m(n)/I_m(1) sim zm-1 to 0$ for all integer $n geq 2$, so the leading von Neumann signal cannot be reconstructed from integer Rényi data.
arXiv Detail & Related papers (2026-03-09T22:27:00Z) - Permanents of matrix ensembles: computation, distribution, and geometry [0.0]
We use the GPU to greaatly accelerate the computation of permanents over $mathbbC,$ $mathbbR,$ $mathbbF_p$ and $mathbbQ.$<n>We study the permanent along geodesics on the unitary group.<n>For the geodesic from the identity to the $n$-cycle permutation matrix, we find a universal scaling function $f(t)=frac1nln|perm((t))|$ that is independent of$n$ in
arXiv Detail & Related papers (2026-02-08T22:31:42Z) - Discrete symmetries in classical and quantum oscillators [51.56484100374058]
We show the eigenfunctions $_n=zn$ of the quantum Hamiltonian in the complex Bargmann-Fock-Segal representation.<n>The superposition $=sum_n c_n_n$ arises only with incomplete knowledge of the initial data for solving the Schrdinger equation.
arXiv Detail & Related papers (2026-01-05T10:04:39Z) - Information-Computation Tradeoffs for Noiseless Linear Regression with Oblivious Contamination [65.37519531362157]
We show that any efficient Statistical Query algorithm for this task requires VSTAT complexity at least $tildeOmega(d1/2/alpha2)$.
arXiv Detail & Related papers (2025-10-12T15:42:44Z) - A Unified Framework for Uniform Signal Recovery in Nonlinear Generative
Compressed Sensing [68.80803866919123]
Under nonlinear measurements, most prior results are non-uniform, i.e., they hold with high probability for a fixed $mathbfx*$ rather than for all $mathbfx*$ simultaneously.
Our framework accommodates GCS with 1-bit/uniformly quantized observations and single index models as canonical examples.
We also develop a concentration inequality that produces tighter bounds for product processes whose index sets have low metric entropy.
arXiv Detail & Related papers (2023-09-25T17:54:19Z) - Learning a Single Neuron with Adversarial Label Noise via Gradient
Descent [50.659479930171585]
We study a function of the form $mathbfxmapstosigma(mathbfwcdotmathbfx)$ for monotone activations.
The goal of the learner is to output a hypothesis vector $mathbfw$ that $F(mathbbw)=C, epsilon$ with high probability.
arXiv Detail & Related papers (2022-06-17T17:55:43Z) - Random matrices in service of ML footprint: ternary random features with
no performance loss [55.30329197651178]
We show that the eigenspectrum of $bf K$ is independent of the distribution of the i.i.d. entries of $bf w$.
We propose a novel random technique, called Ternary Random Feature (TRF)
The computation of the proposed random features requires no multiplication and a factor of $b$ less bits for storage compared to classical random features.
arXiv Detail & Related papers (2021-10-05T09:33:49Z) - Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and
ReLUs under Gaussian Marginals [49.60752558064027]
We study the fundamental problems of agnostically learning halfspaces and ReLUs under Gaussian marginals.
Our lower bounds provide strong evidence that current upper bounds for these tasks are essentially best possible.
arXiv Detail & Related papers (2020-06-29T17:10:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.