[R] [P] Generating Tabular Data with GANs/VAEs for datasets with both Continuous and Discrete Features


I'm not aware of any techniques out there currently which convincingly address this problem. But I have been Googling around for hours and found the following resources.

This has a really nice idea that combines several softmax outputs (one for each discrete feature) with continuous ones at the end of the GAN.


This also mentions using a VAE to generate prototypes for counterfactual explanations, but I don't think it's as relevant.



MIT has a nice repository for doing this also, but it's in Tensorflow, and not easy to take apart for extending it for research etc.


I don't suppose anyone has any suggestions of which of these methods might be best? I'm leaning towards the first one, but surprisingly no one has a published a paper doing it, so I'd have to code it myself and it'd be hard to justify at a conference review process by citing an internet article and saying that "some guy on the internet said it worked well, so we did the same thing here".

I want to use such a generative model for research into explainable AI, but I've never surveyed this literature before, and it's pretty hectic. Thanks for any responses.

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Source: Reddit Machine Learning

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