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Found 5 papers, 3 papers with code
Multi-objective evolutionary GAN for tabular data synthesis
However, there are unique challenges in tabular data compared to images, eg tabular data may contain both continuous and discrete variables and conditional sampling, and, critically, the data should possess high utility and low disclosure risk (the risk of re-identifying a population unit or learning something new about them), providing an opportunity for multi-objective (MO) optimization.
Comparing the Utility and Disclosure Risk of Synthetic Data with Samples of Microdata
The paper presents a framework to measure the utility and disclosure risk of synthetic data by comparing it to samples of the original data of varying sample fractions, thereby identifying the sample fraction which has equivalent utility and risk to the synthetic data.
Generative Adversarial Networks for Synthetic Data Generation: A Comparative Study
Generative Adversarial Networks (GANs) are gaining increasing attention as a means for synthesising data.
Provenance, Anonymisation and Data Environments: a Unifying Construction
Based on this use case, we identify how provenance information could be utilized within the ADF framework, and identify a currently un-met requirement which is the modeling of \textit{data environments}.
Breaking the Activation Function Bottleneck through Adaptive Parameterization
Standard neural network architectures are non-linear only by virtue of a simple element-wise activation function, making them both brittle and excessively large.
