Repeating a question I asked Jesse earlier, since others might be interested in the answer: how come we tend to hear more about PAC bounds than MAC bounds?
I think this mostly has to do with the fact that learning theory grew up in/next to computer science where the focus is usually worst-case performance (esp. in algorithmic complexity theory). This naturally led to the mindset of uniform bounds. That and there’s a bit of historical contingency: people started doing it this way, and early approaches have a habit of sticking.
Repeating a question I asked Jesse earlier, since others might be interested in the answer: how come we tend to hear more about PAC bounds than MAC bounds?
I think this mostly has to do with the fact that learning theory grew up in/next to computer science where the focus is usually worst-case performance (esp. in algorithmic complexity theory). This naturally led to the mindset of uniform bounds. That and there’s a bit of historical contingency: people started doing it this way, and early approaches have a habit of sticking.