You'd be surprised at the vast array of PhDs who've entered this space through conversion courses from essentially non-technical backgrounds -- that end-up repe

You'd be surprised at the vast array of PhDs who've entered this space through conversion courses from essentially non-technical backgrounds -- that end-up repeating essentially mystical pseudoscience. There's lots of papers, vast numbers, that very clearly miss basic premises about the data generating process; and about what exactly we're modelling and how. Premises drilled into statisticians, and so on -- but omitted from a computer science education; and esp. from much of the route into these fields today. Absent these, the process, i'm sure, appears very mystical. This paper ought ask, "what assumptions about the data generating process ought we make such that these regularisation conditions work" I really do think that the question isnt phrased this way due to "too much kool-aid" --- its a paper which seems, to me, to already buy into a kind of mystification. Though, this may just be a symptom of not thinking about the problem from the pov of formal/traditional stats. The paper seems to be wanting some sort of "cute conceptual answer" that might appear in a hand-wavey ML paper But the answer is to specify families of distributions whose semantic and non-sematic structures are "accessible" by these unsupervised techniques -- find which families do not work, which ones do... and then formulate an essentially psych-social hypothesis as to why the people creating htat data have used those distributions