I disagree, every ML model has some implicit statistical assumption, which is often not well understood by practitioners.
At minimum you must assume your underlying process is not fat tailed. If it is, then your training/validation/test data might never be enough to make reliable predictions and your model might break constantly in prod.
BTW shifting distributions and fat tailed distributions are sort of equivalent, at least mathematically.
I don't disagree with any of that, but I still think a responsible, clear-thinking ML practitioner can avoid having to assume the form of the data-generating process, depending on their application.
In some cases if you care about PAC generalization bounds, it's even the case that the bounds do actually hold for all possible distributions.
I think it's more meaningful to have the discussion in a specific problem domain since statistical inference or ML are just tools to better model a problem / phenomenon. The domain (prior) knowledge -- everything else that's not stats / ML, are the keys to build a more robust model. Leave the problem domain out we are left just with pure mathematical theories and the points can only be proved by simulated data.
Comments
I disagree, every ML model has some implicit statistical assumption, which is often not well understood by practitioners.
At minimum you must assume your underlying process is not fat tailed. If it is, then your training/validation/test data might never be enough to make reliable predictions and your model might break constantly in prod.
BTW shifting distributions and fat tailed distributions are sort of equivalent, at least mathematically.
I don't disagree with any of that, but I still think a responsible, clear-thinking ML practitioner can avoid having to assume the form of the data-generating process, depending on their application.
In some cases if you care about PAC generalization bounds, it's even the case that the bounds do actually hold for all possible distributions.
I think it's more meaningful to have the discussion in a specific problem domain since statistical inference or ML are just tools to better model a problem / phenomenon. The domain (prior) knowledge -- everything else that's not stats / ML, are the keys to build a more robust model. Leave the problem domain out we are left just with pure mathematical theories and the points can only be proved by simulated data.