Estimating individual treatment effects (ITE) from observational data is challenging due to
the absence of counterfactuals and the treatment selection bias. Prevalent ITE estimation …

Estimating the individuals' potential response to varying treatment doses is crucial for
decision-making in areas such as precision medicine and management science. Most …

Diffusion models have demonstrated competitive performance in missing data imputation
(MDI) task. However, directly applying diffusion models to MDI produces suboptimal …

Missing data imputation is a critical aspect of the Industrial Internet-of-Things (IIoT), which is
uniquely challenged by local relationships within data due to different operational contexts …

Recommender systems are designed to learn user preferences from observed feedback and
comprise many fundamental tasks, such as rating prediction and post-click conversion rate …

In database of recommender systems, users' ratings for most items are usually missing,
resulting in selection bias when users selectively choose items to rate. To address this …

Enhancing the generalization performance of neural networks remains a formidable
challenge, due to the model selection trade-off between training error and generalization …