3 papers
stat.CO2026
Stochastic Neural Networks for Causal Inference with Missing Confounders
Yaxin Fang, Faming Liang
Unmeasured confounding is a fundamental obstacle to causal inference from observational data. Latent-variable methods address this challenge by imputing unobserved confounders, yet…
stat.ML2025
Uncertainty Quantification for Physics-Informed Neural Networks with Extended Fiducial Inference
Frank Shih, Zhenghao Jiang, Faming Liang
Uncertainty quantification (UQ) in scientific machine learning is increasingly critical as neural networks are widely adopted to tackle complex problems across diverse scientific d…
cs.LG2025
Deep Survival Analysis for Competing Risk Modeling with Functional Covariates and Missing Data Imputation
Penglei Gao, Yan Zou, Abhijit Duggal +3
We introduce the Functional Competing Risk Net (FCRN), a unified deep-learning framework for discrete-time survival analysis under competing risks, which seamlessly integrates func…