17 citations · 74 across the 39 of their papers we have counts for
39 papers
Minimizing the Arithmetic and Communication Complexity of Jacobi's Method for Eigenvalues and Singular Values: Part Two -- Parallel Algorithms
James Demmel, Hengrui Luo, Ryan Schneider +1
This paper presents several parallel versions of Jacobi's method for the symmetric eigenvalue problem and the SVD. A continuation of [Demmel, Luo, Schneider, & Wang 2025], we devel…
Balancing fractional Brownian motion
Hengrui Luo, Yiming Xu
We study the discrepancy of balancing independent sample paths of fractional Brownian motion with Hurst exponent on , an infinite-dimensional analogue of bal…
Spectral Signatures of Large Language Models
Zhuoying Zhang, Ishan V. Prasad, Yuanzhe Hu +4
The rapidly growing repository of publicly available large language models (LLMs) presents significant challenges for systematic management and quantification at scale, such as mod…
Shrinkage priors for Bayesian Substitute Confounders
Yordan P. Raykov, Hengrui Luo, Justin D. Strait +1
Multi-cause observational studies contain information about unmeasured confounding through the dependence structure among causes. However, literal imputation of the unobserved conf…
Bayesian Tensor-on-Tensor Varying Coefficient Model for Forecasting Alzheimer's Disease Progression
Yajie Liu, Hengrui Luo, Suprateek Kundu
We propose a novel tensor-on-tensor modeling framework that flexibly models nonlinear voxel-level relationships using Gaussian process (GP) priors, while incorporating the spatial…
Asymptotic Optimism for Tensor Regression Models with Applications to Neural Network Compression
Haoming Shi, Eric C. Chi, Hengrui Luo
We study rank selection for low-rank tensor regression under random covariates design. Under a Gaussian random-design model and some mild conditions, we derive population expressio…
Wasserstein-type Gaussian Process Regressions for Input Measurement Uncertainty
Hengrui Luo, Xiaoye S. Li, Yang Liu +3
Gaussian process (GP) regression is widely used for uncertainty quantification, yet the standard formulation assumes noise-free covariates. When inputs are measured with error, thi…
GGMPs: Generalized Gaussian Mixture Processes
Vardaan Tekriwal, Mark D. Risser, Hengrui Luo +1
Conditional density estimation is complicated by multimodality, heteroscedasticity, and strong non-Gaussianity. Gaussian processes (GPs) provide a principled nonparametric framewor…
Wedge Sampling: Efficient Tensor Completion with Nearly-Linear Sample Complexity
Hengrui Luo, Anna Ma, Ludovic Stephan +1
We introduce Wedge Sampling, a new non-adaptive sampling scheme for low-rank tensor completion. We study recovery of an order- low-rank tensor of dimension $n\times\cdots\times…
Wavelet Tree Ensembles for Triangulable Manifolds
Hengrui Luo, Akira Horiguchi, Li Ma
We develop unbalanced Haar (UH) wavelet tree ensembles for regression on triangulable manifolds. Given data sampled on a triangulated manifold, we construct UH wavelet trees whose…
Robust Bayesian Optimization via Tempered Posteriors
Jiguang Li, Hengrui Luo
Bayesian optimization (BO) iteratively fits a Gaussian process (GP) surrogate to accumulated evaluations and selects new queries via an acquisition function. Under local misspecifi…
gp2Scale: A Class of Compactly Supported Non-Stationary Kernels and Distributed Computing for Exact Gaussian Processes on 10 Million Data Points
Marcus M. Noack, Mark D. Risser, Hengrui Luo +2
Despite a large corpus of recent work on scaling up Gaussian processes, a stubborn trade-off between computational speed, prediction and uncertainty quantification accuracy, and cu…
Minimizing the Arithmetic and Communication Complexity of Jacobi's Method for Eigenvalues and Singular Values: Part One -- Serial Algorithms
James Demmel, Hengrui Luo, Ryan Schneider +1
We analyze several versions of Jacobi's method for the symmetric eigenvalue problem. Our goal is to reduce the asymptotic cost of the algorithm as much as possible, as measured by…
Tackling Parallelization Challenges of Randomized Preconditioners With Dependency Tracking
Tianyu Liang, Chao Chen, Yotam Yaniv +6
Incomplete Cholesky (IC) preconditioners are the standard black-box, memory-efficient first resort for large graph Laplacian systems, but classical IC can break down on irregular g…
EnQode: Fast Amplitude Embedding for Quantum Machine Learning Using Classical Data
Jason Han, Nicholas S. DiBrita, Younghyun Cho +2
Amplitude embedding (AE) is essential in quantum machine learning (QML) for encoding classical data onto quantum circuits. However, conventional AE methods suffer from deep, variab…
Kernel-based estimators for functional causal effects
Yordan P. Raykov, Hengrui Luo, Justin D. Strait +1
We propose causal effect estimators based on empirical Fréchet means and operator-valued kernels, tailored to functional data spaces. These methods address the challenges of high-d…
Stabilizing the Splits through Minimax Decision Trees
Zhenyuan Zhang, Hengrui Luo
By revisiting the end-cut preference (ECP) phenomenon associated with a single CART (Breiman et al. (1984)), we introduce MinimaxSplit decision trees, a robust alternative to CART…
Asymptotic Optimism of Random-Design Linear and Kernel Regression Models
Hengrui Luo, Yunzhang Zhu
We derived the closed-form asymptotic optimism of linear regression models under random designs, and generalizes it to kernel ridge regression. Using scaled asymptotic optimism as…
Compactly-supported nonstationary kernels for computing exact Gaussian processes on big data
Mark D. Risser, Marcus M. Noack, Hengrui Luo +1
The Gaussian process (GP) is a widely used probabilistic machine learning method with implicit uncertainty characterization for stochastic function approximation, stochastic modeli…
Ranking Perspective for Tree-based Methods with Applications to Symbolic Feature Selection
Hengrui Luo, Meng Li
Tree-based methods are powerful nonparametric techniques in statistics and machine learning. However, their effectiveness, particularly in finite-sample settings, is not fully unde…
Frontal Slice Approaches for Tensor Linear Systems
Hengrui Luo, Anna Ma
Inspired by the row and column action methods for solving large-scale linear systems, in this work, we explore the use of frontal slices for solving tensor linear systems. In parti…
Efficient Decision Trees for Tensor Regressions
Hengrui Luo, Akira Horiguchi, Li Ma
We proposed the tensor-input tree (TT) method for scalar-on-tensor and tensor-on-tensor regression problems. We first address scalar-on-tensor problem by proposing scalar-output re…
Topological Learning for Motion Data via Mixed Coordinates
Hengrui Luo, Jisu Kim, Alice Patania +1
Topology can extract the structural information in a dataset efficiently. In this paper, we attempt to incorporate topological information into a multiple output Gaussian process m…
A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes
Marcus M. Noack, Hengrui Luo, Mark D. Risser
The Gaussian process (GP) is a popular statistical technique for stochastic function approximation and uncertainty quantification from data. GPs have been adopted into the realm of…