24 citations · 66 across the 7 of their papers we have counts for
8 papers
InfMasking: A Unified Variational Framework for Synergistic Multimodal Learning
Liangjian Wen, Qun Dai, Jiang Duan +7
Multimodal models are expected to exploit synergistic information, that is, information available only when multiple modalities are observed together. In practice, however, a fusio…
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…
PITE: Multi-Prototype Alignment for Individual Treatment Effect Estimation
Fuyuan Cao, Jiaxuan Zhang, Xiaoli Li
Estimating Individual Treatment Effects (ITE) from observational data is challenging due to confounding bias. Most studies tackle this bias by balancing distributions globally, but…
Few-Shot Identity Adaptation for 3D Talking Heads via Global Gaussian Field
Hong Nie, Fuyuan Cao, Lu Chen +3
Reconstruction and rendering-based talking head synthesis methods achieve high-quality results with strong identity preservation but are limited by their dependence on identity-spe…
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…
Towards Privacy-Aware Causal Structure Learning in Federated Setting
Jianli Huang, Xianjie Guo, Kui Yu +2
Causal structure learning has been extensively studied and widely used in machine learning and various applications. To achieve an ideal performance, existing causal structure lear…
Towards Efficient Local Causal Structure Learning
Shuai Yang, Hao Wang, Kui Yu +2
Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes…
Learning causal representations for robust domain adaptation
Shuai Yang, Kui Yu, Fuyuan Cao +3
Domain adaptation solves the learning problem in a target domain by leveraging the knowledge in a relevant source domain. While remarkable advances have been made, almost all exist…