[HTML][HTML] Surprises in high-dimensional ridgeless least squares interpolation
Interpolators—estimators that achieve zero training error—have attracted growing attention
in machine learning, mainly because state-of-the art neural networks appear to be models of …
in machine learning, mainly because state-of-the art neural networks appear to be models of …
[BOOK][B] A dynamical approach to random matrix theory
A co-publication of the AMS and the Courant Institute of Mathematical Sciences at New York
University This book is a concise and self-contained introduction of recent techniques to …
University This book is a concise and self-contained introduction of recent techniques to …
Dimension free ridge regression
Dimension free ridge regression Page 1 The Annals of Statistics 2024, Vol. 52, No. 6, 2879–2912
https://doi.org/10.1214/24-AOS2449 © Institute of Mathematical Statistics, 2024 DIMENSION …
https://doi.org/10.1214/24-AOS2449 © Institute of Mathematical Statistics, 2024 DIMENSION …
A goodness-of-fit test for stochastic block models
J Lei - 2016 - projecteuclid.org
The stochastic block model is a popular tool for studying community structures in network
data. We develop a goodness-of-fit test for the stochastic block model. The test statistic is …
data. We develop a goodness-of-fit test for the stochastic block model. The test statistic is …
Universality of approximate message passing with semirandom matrices
Universality of approximate message passing with semirandom matrices Page 1 The
Annals of Probability 2023, Vol. 51, No. 5, 1616–1683 https://doi.org/10.1214/23-AOP1628 …
Annals of Probability 2023, Vol. 51, No. 5, 1616–1683 https://doi.org/10.1214/23-AOP1628 …
Anisotropic local laws for random matrices
We develop a new method for deriving local laws for a large class of random matrices. It is
applicable to many matrix models built from sums and products of deterministic or …
applicable to many matrix models built from sums and products of deterministic or …
Hypothesis testing for automated community detection in networks
Community detection in networks is a key exploratory tool with applications in a diverse set
of areas, ranging from finding communities in social and biological networks to identifying …
of areas, ranging from finding communities in social and biological networks to identifying …
Spectrum of inner-product kernel matrices in the polynomial regime and multiple descent phenomenon in kernel ridge regression
T Misiakiewicz - arxiv preprint arxiv:2204.10425, 2022 - arxiv.org
We study the spectrum of inner-product kernel matrices, ie, $ n\times n $ matrices with
entries $ h (\langle\textbf {x} _i,\textbf {x} _j\rangle/d) $ where the $(\textbf {x} _i) _ {i\leq n} …
entries $ h (\langle\textbf {x} _i,\textbf {x} _j\rangle/d) $ where the $(\textbf {x} _i) _ {i\leq n} …
On the principal components of sample covariance matrices
We introduce a class of M * MM× M sample covariance matrices QQ which subsumes and
generalizes several previous models. The associated population covariance matrix Σ= EQ …
generalizes several previous models. The associated population covariance matrix Σ= EQ …
Optshrink: An algorithm for improved low-rank signal matrix denoising by optimal, data-driven singular value shrinkage
RR Nadakuditi - IEEE Transactions on Information Theory, 2014 - ieeexplore.ieee.org
The truncated singular value decomposition of the measurement matrix is the optimal
solution to the representation problem of how to best approximate a noisy measurement …
solution to the representation problem of how to best approximate a noisy measurement …