1 citations · 1 across the 8 of their papers we have counts for
8 papers
Your Data Manifold is Secretly a Reward Model: Shell-LCC for Text-to-Video Generation
Shihao Zhang, Yunzhi Li, Yuguang Yan +4
Recent text-to-video (T2V) diffusion models rely heavily on auxiliary reward signals (e.g., via reward models or DPO) to align generated content with human aesthetics and improve r…
Improving Deep Regression with Tightness
Shihao Zhang, Yuguang Yan, Angela Yao
For deep regression, preserving the ordinality of the targets with respect to the feature representation improves performance across various tasks. However, a theoretical explanati…
Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption
Weilin Chen, Ruichu Cai, Jie Qiao +2
Estimating causal effects under networked interference from observational data is a crucial yet challenging problem. Most existing methods mainly rely on the networked unconfounded…
Long-term Causal Inference via Modeling Sequential Latent Confounding
Weilin Chen, Ruichu Cai, Yuguang Yan +2
Long-term causal inference is an important but challenging problem across various scientific domains. To solve the latent confounding problem in long-term observational studies, ex…
Estimating Long-term Heterogeneous Dose-response Curve: Generalization Bound Leveraging Optimal Transport Weights
Zeqin Yang, Weilin Chen, Ruichu Cai +7
Long-term treatment effect estimation is a significant but challenging problem in many applications. Existing methods rely on ideal assumptions, such as no unobserved confounders o…
Doubly Robust Causal Effect Estimation under Networked Interference via Targeted Learning
Weilin Chen, Ruichu Cai, Zeqin Yang +4
Causal effect estimation under networked interference is an important but challenging problem. Available parametric methods are limited in their model space, while previous semipar…
Debiased Model-based Interactive Recommendation
Zijian Li, Ruichu Cai, Haiqin Huang +4
Existing model-based interactive recommendation systems are trained by querying a world model to capture the user preference, but learning the world model from historical logged da…
Identifying Semantic Component for Robust Molecular Property Prediction
Zijian Li, Zunhong Xu, Ruichu Cai +5
Although graph neural networks have achieved great success in the task of molecular property prediction in recent years, their generalization ability under out-of-distribution (OOD…