most citedA Comprehensive Analysis of Large Language Model Outputs: Similarity, Diversity, and Bias

3 citations · 6 across the 8 of their papers we have counts for

collaborators

15 papers

cs.LG2025

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…

cs.LG2025

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…

cs.LG20251 cited

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…

cs.AI2025

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…

cs.LG2025

Handling Out-of-Distribution Data: A Survey

Lakpa Tamang, Mohamed Reda Bouadjenek, Richard Dazeley +1

In the field of Machine Learning (ML) and data-driven applications, one of the significant challenge is the change in data distribution between the training and deployment stages,…

cs.LG2025

Far From Sight, Far From Mind: Inverse Distance Weighting for Graph Federated Recommendation

Aymen Rayane Khouas, Mohamed Reda Bouadjenek, Hakim Hacid +1

Graph federated recommendation systems offer a privacy-preserving alternative to traditional centralized recommendation architectures, which often raise concerns about data securit…