2 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…
stat.ML2025
Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting
Soon Hoe Lim, Yijin Wang, Annan Yu +4
Flow matching has recently emerged as a powerful paradigm for generative modeling and has been extended to probabilistic time series forecasting in latent spaces. However, the impa…