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Apologies if I didn't understand the paper, but why do you want to apply diffusion models to tabular datasets in the first place?

Do we think they'll be better than decision trees? Is there some tabular problem that can be handled by diffusion but not trees?

First, they give a novel generation algorithm based on combining trees with diffusion, which trees alone just don't give you.

Second, yes, they think some tabular data will be fit better by their combination of trees with diffusion than just with trees.

You might not want to make a sword out of iron if steel is available, but understanding the relationship between iron and steel is broadly valuable.

I can see the mathematical results are interesting, I was more wondering if there was a practical utility to this TreeFlow thing they built.