4 papers
Dependency-aware synthetic tabular data generation
Chaithra Umesh, Kristian Schultz, Manjunath Mahendra +2
Synthetic tabular data is increasingly used in privacy-sensitive domains such as health care, but existing generative models often fail to preserve inter-attribute relationships. I…
Preserving logical and functional dependencies in synthetic tabular data
Chaithra Umesh, Kristian Schultz, Manjunath Mahendra +2
Dependencies among attributes are a common aspect of tabular data. However, whether existing tabular data generation algorithms preserve these dependencies while generating synthet…
Convex space learning for tabular synthetic data generation
Manjunath Mahendra, Chaithra Umesh, Saptarshi Bej +2
Generating synthetic samples from the convex space of the minority class is a popular oversampling approach for imbalanced classification problems. Recently, deep-learning approach…
ConvGeN: Convex space learning improves deep-generative oversampling for tabular imbalanced classification on smaller datasets
Kristian Schultz, Saptarshi Bej, Waldemar Hahn +3
Data is commonly stored in tabular format. Several fields of research are prone to small imbalanced tabular data. Supervised Machine Learning on such data is often difficult due to…