I think Bayesian optimization still makes sense with infinite compute if you have limited data (infinite compute doesn’t imply perfect knowledge, you still have to run experiments in the world outside of your computer).
The reason I specified evolutionary search is because that’s the claim I see Lehman & Stanley as making—that algorithms pursuing simple objectives tend to not be robust in an evolutionary sense. I’m less confident making claims about broader classes of optimization but not intentionally excluding them
Meta point: it feels like we’re bouncing between incompatible and partly-specified formalisms before we even know what the high level worldview diff is.
To that end, I’m curious what you think the implications of the Lehman & Stanley hypothesis would be—supposing it were shown even for architectures that allow planning to search, which I agree their paper does not do. So yes you can trivially exhibit a “goal-oriented search over good search policies” that does better than their naive novelty search, but what if it turns out a “novelty-oriented search over novelty-oriented search policies” does better still? Would this be a crux for you, or is this not even a coherent hypothetical in your ontology of optimization?
Minor points just to get them out of the way:
I think Bayesian optimization still makes sense with infinite compute if you have limited data (infinite compute doesn’t imply perfect knowledge, you still have to run experiments in the world outside of your computer).
The reason I specified evolutionary search is because that’s the claim I see Lehman & Stanley as making—that algorithms pursuing simple objectives tend to not be robust in an evolutionary sense. I’m less confident making claims about broader classes of optimization but not intentionally excluding them
Meta point: it feels like we’re bouncing between incompatible and partly-specified formalisms before we even know what the high level worldview diff is.
To that end, I’m curious what you think the implications of the Lehman & Stanley hypothesis would be—supposing it were shown even for architectures that allow planning to search, which I agree their paper does not do. So yes you can trivially exhibit a “goal-oriented search over good search policies” that does better than their naive novelty search, but what if it turns out a “novelty-oriented search over novelty-oriented search policies” does better still? Would this be a crux for you, or is this not even a coherent hypothetical in your ontology of optimization?