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Found 3 papers, 2 papers with code
ciDATGAN: Conditional Inputs for Tabular GANs
Then, we demonstrate that ciDATGAN can be used to unbias datasets with the help of well-chosen conditional inputs.
DATGAN: Integrating expert knowledge into deep learning for synthetic tabular data
We show that the best versions of the DATGAN outperform state-of-the-art generative models on multiple case studies.
Estimation of discrete choice models with hybrid stochastic adaptive batch size algorithms
The integration of the new algorithms in Discrete Choice Models estimation software will significantly reduce the time required for model estimation and therefore enable researchers and practitioners to explore new approaches for the specification of choice models.
Stochastic Optimization
Optimization and Control
