AI RESEARCH
Wasserstein-type Gaussian Process Regressions for Input Measurement Uncertainty
arXiv CS.LG
•
ArXi:2603.17271v1 Announce Type: cross Gaussian process (GP) regression is widely used for uncertainty quantification, yet the standard formulation assumes noise-free covariates. When inputs are measured with error, this errors-in-variables (EIV) setting can lead to optimistically narrow posterior intervals and biased decisions. We study GP regression under input measurement uncertainty by representing each noisy input as a probability measure and defining covariance through Wasserstein distances between these measures.