The theorem in Cyan’s link assumes that the output of each predictor is a single prediction. If it were instead a probability distribution function over predictions, can we again find an optimal algorithm? If so then it would seem like the only remaining trick would be to get specialized algorithms to output higher uncertainty predictions when facing questions further from their “area”.
Say you need to plan an expedition, like columbus. How much time should you spend shmoozing with royalty to get more money, how much time inspecting the ships, how much testing the crew, etc… and how do these all interact? The narrow predictors would domain specific questions, but you need to be able to meld and balance the info in some way.
The theorem in Cyan’s link assumes that the output of each predictor is a single prediction. If it were instead a probability distribution function over predictions, can we again find an optimal algorithm? If so then it would seem like the only remaining trick would be to get specialized algorithms to output higher uncertainty predictions when facing questions further from their “area”.
Say you need to plan an expedition, like columbus. How much time should you spend shmoozing with royalty to get more money, how much time inspecting the ships, how much testing the crew, etc… and how do these all interact? The narrow predictors would domain specific questions, but you need to be able to meld and balance the info in some way.