Reducibility and statistical-computational gaps from secret leakage
M Brennan, G Bresler - Conference on Learning Theory, 2020 - proceedings.mlr.press
Inference problems with conjectured statistical-computational gaps are ubiquitous
throughout modern statistics, computer science, statistical physics and discrete probability …
throughout modern statistics, computer science, statistical physics and discrete probability …
The role of regularization in classification of high-dimensional noisy gaussian mixture
We consider a high-dimensional mixture of two Gaussians in the noisy regime where even
an oracle knowing the centers of the clusters misclassifies a small but finite fraction of the …
an oracle knowing the centers of the clusters misclassifies a small but finite fraction of the …
Constrained low-rank matrix estimation: Phase transitions, approximate message passing and applications
This article is an extended version of previous work of Lesieur et al (2015 IEEE Int. Symp. on
Information Theory Proc. pp 1635–9 and 2015 53rd Annual Allerton Conf. on …
Information Theory Proc. pp 1635–9 and 2015 53rd Annual Allerton Conf. on …
Massive unsourced random access based on uncoupled compressive sensing: Another blessing of massive MIMO
We put forward a new algorithmic solution to the massive unsourced random access (URA)
problem, by leveraging the rich spatial dimensionality offered by large-scale antenna arrays …
problem, by leveraging the rich spatial dimensionality offered by large-scale antenna arrays …
Information-theoretic bounds and phase transitions in clustering, sparse PCA, and submatrix localization
We study the problem of detecting a structured, low-rank signal matrix corrupted with
additive Gaussian noise. This includes clustering in a Gaussian mixture model, sparse PCA …
additive Gaussian noise. This includes clustering in a Gaussian mixture model, sparse PCA …
Spectral phase transitions in non-linear wigner spiked models
We study the asymptotic behavior of the spectrum of a random matrix where a non-linearity
is applied entry-wise to a Wigner matrix perturbed by a rank-one spike with independent and …
is applied entry-wise to a Wigner matrix perturbed by a rank-one spike with independent and …
Information-theoretic limits for the matrix tensor product
G Reeves - IEEE Journal on Selected Areas in Information …, 2020 - ieeexplore.ieee.org
This article studies a high-dimensional inference problem involving the matrix tensor product
of random matrices. This problem generalizes a number of contemporary data science …
of random matrices. This problem generalizes a number of contemporary data science …
Low-rank matrix estimation with inhomogeneous noise
We study low-rank matrix estimation for a generic inhomogeneous output channel through
which the matrix is observed. This generalizes the commonly considered spiked matrix …
which the matrix is observed. This generalizes the commonly considered spiked matrix …
Statistical problems with planted structures: Information-theoretical and computational limits
This chapter provides a survey of the common techniques for determining the sharp
statistical and computational limits in high-dimensional statistical problems with planted …
statistical and computational limits in high-dimensional statistical problems with planted …
Fundamental limits of low-rank matrix estimation with diverging aspect ratios
Fundamental limits of low-rank matrix estimation with diverging aspect ratios Page 1 The Annals
of Statistics 2024, Vol. 52, No. 4, 1460–1484 https://doi.org/10.1214/24-AOS2400 © Institute of …
of Statistics 2024, Vol. 52, No. 4, 1460–1484 https://doi.org/10.1214/24-AOS2400 © Institute of …