3 papers
stat.ME2026
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…
cs.LG2026
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…
cs.LG2025
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…