research
∙
02/02/2023
The Power of Preconditioning in Overparameterized Low-Rank Matrix Sensing
We propose , a preconditioned gradient descent method to tackle the low-...
share
research
∙
11/23/2022
Empirical Bayes estimation: When does g-modeling beat f-modeling in theory (and in practice)?
Empirical Bayes (EB) is a popular framework for large-scale inference th...
research
∙
06/16/2022
Universality of regularized regression estimators in high dimensions
The Convex Gaussian Min-Max Theorem (CGMT) has emerged as a prominent th...
research
∙
12/04/2021
Nonparametric mixture MLEs under Gaussian-smoothed optimal transport distance
The Gaussian-smoothed optimal transport (GOT) framework, pioneered in Go...
research
∙
10/08/2021
Uncertainty quantification in the Bradley-Terry-Luce model
The Bradley-Terry-Luce (BTL) model is a benchmark model for pairwise com...
research
∙
06/14/2021
Generalized kernel distance covariance in high dimensions: non-null CLTs and power universality
Distance covariance is a popular dependence measure for two random vecto...
research
∙
01/26/2021
A general method for power analysis in testing high dimensional covariance matrices
Covariance matrix testing for high dimensional data is a fundamental pro...
research
∙
10/07/2020
High dimensional asymptotics of likelihood ratio tests in Gaussian sequence model under convex constraint
In the Gaussian sequence model Y=μ+ξ, we study the likelihood ratio test...
research
∙
04/23/2020
On a phase transition in general order spline regression
In the Gaussian sequence model Y= θ_0 + ε in R^n, we study the fundament...
research
∙
01/05/2020
Exponential inequalities for dependent V-statistics via random Fourier features
We establish exponential inequalities for a class of V-statistics under ...
research
∙
02/27/2019
Optimal estimation of variance in nonparametric regression with random design
Consider the heteroscedastic nonparametric regression model with random ...
research
∙
02/07/2019
