1 citations · 2 across the 9 of their papers we have counts for
9 papers
Top Singular Value in Sum-Products of Random Matrices
Kevin Han Huang, Boris Hanin
We study the top singular value for a sum of independent random matrices, each of which is a product of i.i.d. Gaussian matrices. Our main conceptu…
The Zero Pattern of a Design Matrix Drives Multiple Descent in Over-parameterized Regression
Kevin Han Huang, Haoyu Ye, Somak Laha +1
Over-parameterized linear regression has been widely studied over the last decade. However, most existing works assume that the covariates are independent and that their covariance…
Data augmented bootstrap: Unifying confidence interval construction by approximate invariance
Kevin Han Huang
We propose the data augmented bootstrap (DAB), a framework for constructing confidence intervals from approximately invariant transformations of the data. As special cases, DAB rec…
Universality of High-Dimensional Logistic Regression and a Novel CGMT under Dependence with Applications to Data Augmentation
Matthew Esmaili Mallory, Kevin Han Huang, Morgane Austern
Over the last decade, a wave of research has characterized the exact asymptotic risk of many high-dimensional models in the proportional regime. Two foundational results have drive…
Diagonal Symmetrization of Neural Network Solvers for the Many-Electron Schrödinger Equation
Kevin Han Huang, Ni Zhan, Elif Ertekin +2
Incorporating group symmetries into neural networks has been a cornerstone of success in many AI-for-science applications. Diagonal groups of isometries, which describe the invaria…
Slow rates of approximation of U-statistics and V-statistics by quadratic forms of Gaussians
Kevin Han Huang, Peter Orbanz
We construct examples of degree-two U- and V-statistics of i.i.d.~heavy-tailed random vectors in , whose -th moments exist for , and provide tight…
Gaussian universality for approximately polynomial functions of high-dimensional data
Kevin Han Huang, Morgane Austern, Peter Orbanz
Gaussian universality results assert that the properties of many estimators remain unchanged when the input data are replaced by Gaussians. Such results have gained popularity in h…
A High-dimensional Convergence Theorem for U-statistics with Applications to Kernel-based Testing
Kevin H. Huang, Xing Liu, Andrew B. Duncan +1
We prove a convergence theorem for U-statistics of degree two, where the data dimension is allowed to scale with sample size . We find that the limiting distribution of a U-…
Gaussian and Non-Gaussian Universality of Data Augmentation
Kevin Han Huang, Peter Orbanz, Morgane Austern
We provide universality results that quantify how data augmentation affects the variance and limiting distribution of estimates through simple surrogates, and analyze several speci…