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stat.ME2026
Adaptive Nyström for Gaussian Process Regression
Lulu Kang
Gaussian Process Regression (GPR) is a robust framework for uncertainty quantification, yet its complexity limits its scalability. Low-rank Nyström approximations can redu…
stat.ME2026
Robust and Sparse Generalized Linear Models for High-Dimensional Data via Maximum Mean Discrepancy
Xiaoning Kang, Lulu Kang
High-dimensional datasets are frequently subject to contamination by outliers and heavy-tailed noise, which can severely bias standard regularized estimators like the Lasso. While…
stat.ME2025
Bayesian Bridge Gaussian Process Regression
Minshen Xu, Shiwei Lan, Lulu Kang
The performance of Gaussian Process (GP) regression is often hampered by the curse of dimensionality, which inflates computational cost and reduces predictive power in high-dimensi…