7 papers
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…
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…
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…
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…
Robust Analysis for Resilient AI System
Yu Wang, Ran Jin, Lulu Kang
Operational hazards in Manufacturing Industrial Internet (MII) systems generate severe data outliers that cripple traditional statistical analysis. This paper proposes a novel robu…
Optimal Kernel Learning for Gaussian Process Models with High-Dimensional Input
Lulu Kang, Minshen Xu
Gaussian process (GP) regression is a popular surrogate modeling tool for computer simulations in engineering and scientific domains. However, it often struggles with high computat…
Fair Multivariate Adaptive Regression Splines for Ensuring Equity and Transparency
Parian Haghighat, Denisa G'andara, Lulu Kang +1
Predictive analytics is widely used in various domains, including education, to inform decision-making and improve outcomes. However, many predictive models are proprietary and ina…