3 papers
cs.LG2026
Riemannian Graph Tokenizer for Structural Knowledge Transfer
Qimin Zhou, Haibo Liu, Yujie Wang +2
Foundation models are at the forefront of artificial intelligence. A tokenizer, converting the raw input into discrete representations that the model can understand, plays an impor…
cs.LG2026
Disentangled Double Machine Learning for Accurate Causal Effect Estimation
Guodu Xiang, Kui Yu, Yujie Wang +3
Confounding bias is a key challenge in causal effect estimation from observational data. Double Machine Learning (DML) addresses this issue by estimating treatment and outcome nuis…
cs.LG2024
Towards Effective Graph Rationalization via Boosting Environment Diversity
Yujie Wang, Kui Yu, Yuhong Zhang +2
Graph Neural Networks (GNNs) perform effectively when training and testing graphs are drawn from the same distribution, but struggle to generalize well in the face of distribution…