22 citations · 42 across the 9 of their papers we have counts for
9 papers
When does Gaussian equivalence fail and how to fix it: Non-universal behavior of random features with quadratic scaling
Garrett G. Wen, Hong Hu, Yue M. Lu +2
A major effort in modern high-dimensional statistics has been devoted to the analysis of linear predictors trained on nonlinear feature embeddings via empirical risk minimization (…
Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime
Hong Hu, Yue M. Lu, Theodor Misiakiewicz
Recent advances in machine learning have been achieved by using overparametrized models trained until near interpolation of the training data. It was shown, e.g., through the doubl…
Homophily modulates double descent generalization in graph convolution networks
Cheng Shi, Liming Pan, Hong Hu +1
Graph neural networks (GNNs) excel in modeling relational data such as biological, social, and transportation networks, but the underpinnings of their success are not well understo…
Sharp Asymptotics of Kernel Ridge Regression Beyond the Linear Regime
Hong Hu, Yue M. Lu
The generalization performance of kernel ridge regression (KRR) exhibits a multi-phased pattern that crucially depends on the scaling relationship between the sample size and t…
Precise Learning Curves and Higher-Order Scaling Limits for Dot Product Kernel Regression
Lechao Xiao, Hong Hu, Theodor Misiakiewicz +2
As modern machine learning models continue to advance the computational frontier, it has become increasingly important to develop precise estimates for expected performance improve…
Universality Laws for High-Dimensional Learning with Random Features
Hong Hu, Yue M. Lu
We prove a universality theorem for learning with random features. Our result shows that, in terms of training and generalization errors, a random feature model with a nonlinear ac…
The Limiting Poisson Law of Massive MIMO Detection with Box Relaxation
Hong Hu, Yue M. Lu
Estimating a binary vector from noisy linear measurements is a prototypical problem for MIMO systems. A popular algorithm, called the box-relaxation decoder, estimates the target s…
SLOPE for Sparse Linear Regression:Asymptotics and Optimal Regularization
Hong Hu, Yue M. Lu
In sparse linear regression, the SLOPE estimator generalizes LASSO by penalizing different coordinates of the estimate according to their magnitudes. In this paper, we present a pr…
A Solvable High-Dimensional Model of GAN
Chuang Wang, Hong Hu, Yue M. Lu
We present a theoretical analysis of the training process for a single-layer GAN fed by high-dimensional input data. The training dynamics of the proposed model at both microscopic…