1 citations · 1 across the 4 of their papers we have counts for
5 papers
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…
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…
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…
Causal-StoNet: Causal Inference for High-Dimensional Complex Data
Yaxin Fang, Faming Liang
With the advancement of data science, the collection of increasingly complex datasets has become commonplace. In such datasets, the data dimension can be extremely high, and the un…
Fast Value Tracking for Deep Reinforcement Learning
Frank Shih, Faming Liang
Reinforcement learning (RL) tackles sequential decision-making problems by creating agents that interacts with their environment. However, existing algorithms often view these prob…