1 citations · 1 across the 4 of their papers we have counts for
7 papers
IVGAE: Handling Incomplete Heterogeneous Data with a Variational Graph Autoencoder
Youran Zhou, Mohamed Reda Bouadjenek, Sunil Aryal%
Handling missing data remains a fundamental challenge in real-world tabular datasets, especially when data are heterogeneous with both numerical and categorical features. Existing…
MissHDD: Hybrid Deterministic Diffusion for Hetrogeneous Incomplete Data Imputation
Youran Zhou, Mohamed Reda Bouadjenek, Sunil Aryal
Incomplete data are common in real-world tabular applications, where numerical, categorical, and discrete attributes coexist within a single dataset. This heterogeneous structure p…
MissMecha: An All-in-One Python Package for Studying Missing Data Mechanisms
Youran Zhou, Mohamed Reda Bouadjenek, Sunil Aryal
Incomplete data is a persistent challenge in real-world datasets, often governed by complex and unobservable missing mechanisms. Simulating missingness has become a standard approa…
MissDDIM: Deterministic and Efficient Conditional Diffusion for Tabular Data Imputation
Youran Zhou, Mohamed Reda Bouadjenek, Sunil Aryal
Diffusion models have recently emerged as powerful tools for missing data imputation by modeling the joint distribution of observed and unobserved variables. However, existing meth…
Synthesizing Tabular Data Using Selectivity Enhanced Generative Adversarial Networks
Youran Zhou, Jianzhong Qi
As E-commerce platforms face surging transactions during major shopping events like Black Friday, stress testing with synthesized data is crucial for resource planning. Most recent…
Developing robust methods to handle missing data in real-world applications effectively
Youran Zhou, Mohamed Reda Bouadjenek, Sunil Aryal
Missing data is a pervasive challenge spanning diverse data types, including tabular, sensor data, time-series, images and so on. Its origins are multifaceted, resulting in various…