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20232026
most citedMixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space

23 citations · 24 across the 14 of their papers we have counts for

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9 papers · 1 filter

cs.LG2025

A Diffusion-Based Method for Learning the Multi-Outcome Distribution of Medical Treatments

Yuchen Ma, Jonas Schweisthal, Hengrui Zhang +1

In medicine, treatments often influence multiple, interdependent outcomes, such as primary endpoints, complications, adverse events, or other secondary endpoints. Hence, to make op…

cs.LG2025

TabGen-ICL: Residual-Aware In-Context Example Selection for Tabular Data Generation

Liancheng Fang, Aiwei Liu, Hengrui Zhang +3

Large Language models (LLMs) have achieved encouraging results in tabular data generation. However, existing approaches require fine-tuning, which is computationally expensive. Thi…

cs.LG2024

Diffusion-nested Auto-Regressive Synthesis of Heterogeneous Tabular Data

Hengrui Zhang, Liancheng Fang, Qitian Wu +1

Autoregressive models are predominant in natural language generation, while their application in tabular data remains underexplored. We posit that this can be attributed to two fac…

cs.LG2024

Multi-Continental Healthcare Modelling Using Blockchain-Enabled Federated Learning

Rui Sun, Zhipeng Wang, Hengrui Zhang +5

One of the biggest challenges of building artificial intelligence (AI) model in the healthcare area is the data sharing. Since healthcare data is private, sensitive, and heterogene…

cs.LG2024★ 1 cited

SGFormer: Single-Layer Graph Transformers with Approximation-Free Linear Complexity

Qitian Wu, Kai Yang, Hengrui Zhang +2

Learning representations on large graphs is a long-standing challenge due to the inter-dependence nature. Transformers recently have shown promising performance on small graphs tha…

cs.LG2024

DiffPuter: Empowering Diffusion Models for Missing Data Imputation

Hengrui Zhang, Liancheng Fang, Qitian Wu +1

Generative models play an important role in missing data imputation in that they aim to learn the joint distribution of full data. However, applying advanced deep generative models…