130 citations · 263 across the 12 of their papers we have counts for
12 papers
Towards Designing Optimal Sensing Matrices for Generalized Linear Inverse Problems
Junjie Ma, Ji Xu, Arian Maleki
We consider an inverse problem , where is the signal of interest, is the sensing matrix, is a nonlinear fun…
On the proliferation of support vectors in high dimensions
Daniel Hsu, Vidya Muthukumar, Ji Xu
The support vector machine (SVM) is a well-established classification method whose name refers to the particular training examples, called support vectors, that determine the maxim…
When Does Preconditioning Help or Hurt Generalization?
Shun-ichi Amari, Jimmy Ba, Roger Grosse +5
While second order optimizers such as natural gradient descent (NGD) often speed up optimization, their effect on generalization has been called into question. This work presents a…
On the Optimal Weighted Regularization in Overparameterized Linear Regression
Denny Wu, Ji Xu
We consider the linear model with in the overparameterized regime . We estimate $\ma…
On the number of variables to use in principal component regression
Ji Xu, Daniel Hsu
We study least squares linear regression over uncorrelated Gaussian features that are selected in order of decreasing variance. When the number of selected features is at m…
Two models of double descent for weak features
Mikhail Belkin, Daniel Hsu, Ji Xu
The "double descent" risk curve was proposed to qualitatively describe the out-of-sample prediction accuracy of variably-parameterized machine learning models. This article provide…
Spectral Method for Phase Retrieval: an Expectation Propagation Perspective
Junjie Ma, Rishabh Dudeja, Ji Xu +2
Phase retrieval refers to the problem of recovering a signal from its phaseless measurements …
Consistent Risk Estimation in Moderately High-Dimensional Linear Regression
Ji Xu, Arian Maleki, Kamiar Rahnama Rad +1
Risk estimation is at the core of many learning systems. The importance of this problem has motivated researchers to propose different schemes, such as cross validation, generalize…
Benefits of over-parameterization with EM
Ji Xu, Daniel Hsu, Arian Maleki
Expectation Maximization (EM) is among the most popular algorithms for maximum likelihood estimation, but it is generally only guaranteed to find its stationary points of the log-l…
Approximate Message Passing for Amplitude Based Optimization
Junjie Ma, Ji Xu, Arian Maleki
We consider an -regularized non-convex optimization problem for recovering signals from their noisy phaseless observations. We design and study the performance of a message…
Optimization-based AMP for Phase Retrieval: The Impact of Initialization and -regularization
Junjie Ma, Ji Xu, Arian Maleki
We consider an -regularized non-convex optimization problem for recovering signals from their noisy phaseless observations. We design and study the performance of a message…
Global analysis of Expectation Maximization for mixtures of two Gaussians
Ji Xu, Daniel Hsu, Arian Maleki
Expectation Maximization (EM) is among the most popular algorithms for estimating parameters of statistical models. However, EM, which is an iterative algorithm based on the maximu…