A lot of ML research is done by PhDs in other fields. I did research that focused on developing compact group invariant features (for neural networks) for predicting local atomic energies in materials science, and a few mathematicians I follow did work on developing convolutional neural networks that utilize Clebsch–Gordan coefficients to generalize the translational invariance to other symmetries.

On the contrary, a lot of CS machine learning research is heavily application focused (generate such-and-such new thing using a GAN). If anything, mathematicians are the ones who understand machine learning at the very deepest level of theory. This isn't to say there aren't many theory-focused CS presentations/publications; I'm just refuting your point that highly theoretical machine learning research is purely the domain of CS PhDs.