Enjoying reading this. Clarifies some nice connections between scoring rules, probabilistic divergences, convex analysis, and so on. Should read it even more closely, to be honest!
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I had heard of it / seen it cited lots before, but I think that I had the wrong impression of what type of paper it was. It's very dense with new information, pay-offs, etc. in a way that I didn't necessarily expect (even as somebody who knows many of the objects involved).
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It's really well written and if you replaced "students" with "models" in the text, would read like a fairly modern machine learning paper
https://link.springer.com/article/10.1007/BF02289503