4 papers
Spectral Dependence of Convex Regularization: Fundamental Limits under Right-Rotationally Invariant Designs
Baichen Tan, Audrey Yang, Cynthia Rush
We study the fundamental limits of convex-regularized estimation in high-dimensional linear regression with right-rotationally invariant design matrices. We show that the asymptoti…
Sharp Trade-Offs in High-Dimensional Inference via 2-Level SLOPE
Zhiqi Bu, Jason M. Klusowski, Cynthia Rush +1
Among techniques for high-dimensional linear regression, Sorted L-One Penalized Estimation (SLOPE) generalizes the LASSO via an adaptive regularization that applies heavier p…
Generalized Linear Models with 1-Bit Measurements: Asymptotics of the Maximum Likelihood Estimator
Jaimin Shah, Martina Cardone, Cynthia Rush +1
This work establishes regularity conditions for consistency and asymptotic normality of the multiple parameter maximum likelihood estimator(MLE) from censored data, where the censo…
Max-sliced Wasserstein concentration and uniform ratio bounds of empirical measures on RKHS
Ruiyu Han, Cynthia Rush, Johannes Wiesel
Optimal transport and the Wasserstein distance have recently seen a number of applications in the fields of statistics, machine learning, data science, and the phys…