8 citations · 22 across the 11 of their papers we have counts for
11 papers
High-Dimensional Statistics: Reflections on Progress and Open Problems
Arian Maleki, Subhabrata Sen, Sivaraman Balakrishnan +9
Over the past two decades, the field of high-dimensional statistics has experienced substantial progress, driven largely by technological advances that have dramatically reduced th…
High-Dimensional Asymptotics of Differentially Private PCA
Youngjoo Yun, Rishabh Dudeja
In differential privacy, random noise is introduced to privatize summary statistics of a sensitive dataset before releasing them. The noise level determines the privacy loss, which…
Optimality of Approximate Message Passing Algorithms for Spiked Matrix Models with Rotationally Invariant Noise
Rishabh Dudeja, Songbin Liu, Junjie Ma
We study the problem of estimating a rank one signal matrix from an observed matrix generated by corrupting the signal with additive rotationally invariant noise. We develop a new…
Spectral Universality of Regularized Linear Regression with Nearly Deterministic Sensing Matrices
Rishabh Dudeja, Subhabrata Sen, Yue M. Lu
It has been observed that the performances of many high-dimensional estimation problems are universal with respect to underlying sensing (or design) matrices. Specifically, matrice…
Statistical-Computational Trade-offs in Tensor PCA and Related Problems via Communication Complexity
Rishabh Dudeja, Daniel Hsu
Tensor PCA is a stylized statistical inference problem introduced by Montanari and Richard to study the computational difficulty of estimating an unknown parameter from higher-orde…
Universality of Approximate Message Passing with Semi-Random Matrices
Rishabh Dudeja, Yue M. Lu, Subhabrata Sen
Approximate Message Passing (AMP) is a class of iterative algorithms that have found applications in many problems in high-dimensional statistics and machine learning. In its gener…