I think it would be interesting if frequentist stats can come up with more generative models. Current high level generative machine learning all rely on Bayesian modeling.
I'm not well versed enough, but what would a frequentist generative model even mean?
The entire generative concept implicitly assumes that parameters have probability distributions themselves that naturally give rise to generative models...
You could do frequentist inference on a generative model, sure, but generative modelling seems fundamentally alien to frequentist thinking?
I am more familiar with Bayesian than frequentist stats, but given that they are mathematically equivalent, shouldn't frequentist stats have an answer to e.g. the loss function of a VAE? Or are generative machine learning inherently impossible to model for frequentist stats?
Though if you think about it, a diffusion model is somewhat (partially) frequentist.
I guess you have me thinking more... things like Parzen window estimators or other KDEs are frequentist...
But while it's a probability distribution, to a frequentist they are estimating the fixed parameters of a distribution.
The distribution isn't generative, it just represents uncertainty - and I think that's a bit of the deep core philosophical divide between frequentists and Bayesians - you might use all the same math, but you cannot possibly think of it as being generative.