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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…
cs.LG2026
SAFE-SVD: Sensitivity-Aware Fidelity-Enforcing SVD for Physics Foundation Models
Chengjie Hong, Feixiang He, Yiheng Zeng +2
We propose a new method for compressing physics foundation models (PFMs) which is a new trend in AI for Science. While model compression is essential for reducing memory use and ac…
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…