mlsanityData to 7 Oct 2026

Fundamental limits of detection in the spiked Wigner model

arXiv 1806.09588 ↗First posted 25 Jun 2018math.PR math.ST stat.MLcited by 18

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Abstract (arXiv)

We study the fundamental limits of detecting the presence of an additive rank-one perturbation, or spike, to a Wigner matrix. When the spike comes from a prior that is i.i.d. across coordinates, we prove that the log-likelihood ratio of the spiked model against the non-spiked one is asymptotically normal below a certain reconstruction threshold which is not necessarily of a "spectral" nature, and that it is degenerate above. This establishes the maximal region of contiguity between the planted and null models. It is known that this threshold also marks a phase transition for estimating the spike: the latter task is possible above the threshold and impossible below. Therefore, both estimation and detection undergo the same transition in this random matrix model. We also provide further information about the performance of the optimal test. Our proofs are based on Gaussian interpolation methods and a rigorous incarnation of the cavity method, as devised by Guerra and Talagrand in their study of the Sherrington--Kirkpatrick spin-glass model.

Key facts. Cited by 18 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Jun 2018.

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Citing papers per year: 2021: 1; 2022: 5; 2023: 5; 2024: 5; 2025: 2.

18 papers.

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  1. 2022High-dimensional limit theorems for SGD: Effective dynamics and critical scalingcited by 69
  2. 2022The Franz-Parisi Criterion and Computational Trade-offs in High Dimensional Statisticscited by 31
  3. 2023Optimal Algorithms for the Inhomogeneous Spiked Wigner Modelcited by 8
  4. 2024Information limits and Thouless-Anderson-Palmer equations for spiked matrix models with structured noisecited by 6
  5. 2021Detection of Signal in the Spiked Rectangular Modelscited by 4
  6. 2023Precise Error Rates for Computationally Efficient Testingcited by 4
  7. 2024Spectral Phase Transition and Optimal PCA in Block-Structured Spiked modelscited by 4
  8. 2022Bayes-optimal limits in structured PCA, and how to reach themcited by 3
  9. 2023Learning from higher-order statistics, efficiently: hypothesis tests, random features, and neural networkscited by 3
  10. 2023The Decimation Scheme for Symmetric Matrix Factorizationcited by 2
  11. 2024Sharp Analysis of Power Iteration for Tensor PCAcited by 2
  12. 2024Fundamental limits of Non-Linear Low-Rank Matrix Estimationcited by 2
6 more
  1. 2024Signal-Plus-Noise Decomposition of Nonlinear Spiked Random Matrix Modelscited by 2
  2. 2022Asymptotic Normality of Log Likelihood Ratio and Fundamental Limit of the Weak Detection for Spiked Wigner Matricescited by 1
  3. 2022Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gapcited by 1
  4. 2023Detection problems in the spiked matrix modelscited by 1
  5. 2025Learning with Restricted Boltzmann Machines: Asymptotics of AMP and GD in High Dimensionscited by 0
  6. 2025Computational and statistical lower bounds for low-rank estimation under general inhomogeneous noisecited by 0