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 …

A well-trained deep neural network on balanced datasets usually exhibits the Neural
Collapse (NC) phenomenon, which is an informative indicator of the model achieving good …

We study the problem of missing data imputation, which is a fundamental task in the area of
data quality that aims to impute the missing data to achieve the completeness of datasets …

Accurate imputation of missing data is pivotal in real-world industrial applications.
Traditional direct imputers, which utilize basic statistics to replace missing elements, offer a …

Exploring the missing values is an essential but challenging issue due to the complex latent
spatio-temporal correlation and dynamic nature of time series. Owing to the outstanding …

Mobile CrowdSensing (MCS) has emerged as a popular paradigm to engage mobile users
in collaborative sensing tasks. However, its performance is hindered by its limited …

Causal discovery in the presence of missing data introduces a chicken-and-egg dilemma.
While the goal is to recover the true causal structure, robust imputation requires considering …

The mining of diverse patterns from bike flow has attracted widespread interest from
researchers and practitioners. Prior arts concentrate on forecasting the flow evolution from …

Relational data play a crucial role in various fields, but they are often plagued by low-quality
issues such as erroneous and missing values, which can terribly impact downstream …