Hey, I'm Owen.
I think rationality is pretty rad.
ah yes, the proof of stake bridge is faster.
i guess it depends if you're running this strategy with size. e.g. for over $100,000, 10% returns means you'd earn back gas fees in ~3 days.
fyi you can get around half these returns on aave on ethereum mainnet without having to mess with matic at all.
while i don't think the matic team is untrustworthy, it's worth pointing out their entire network is currently secured by an upgradeable multisig wallet.
there is also a ~1 week period to move back from matic to ethereum mainnet which can be irksome if you e.g. want to sell quickly back to fiat via some centralized exchange.
Just chiming in here to say that I completely forgot about Intercom during this entire series of events, and I wish I had remembered/used it earlier.
(I disabled the button a long time ago, and it has been literal years since I used it last.)
Thanks for this summary of our post.
I think one other sub-field that has seen a lot of progress is in creating somewhat competitive models that are inherently more interpretable (i.e. a lot of the augmented/approximate decision tree models), as well as some of the decision set stuff.
Otherwise, I think it's a fair assessment, will also link this comment to Peter so he can chime in with any suggested clarifications of our opinions, if any.
Ah, I didn't mean to ask about the designing part, but moreso about how you use the word optimize in your definition when it comes to 'optimizing from scratch', which might get a little recursive.
Your definition of optimizer uses "optimizing that function from scratch" which might need some more unpacking.
You may be interested in this prior discussion on optimization which shares some things with your definition but takes a more control theory / systems perspective.
I have not read the book, perhaps Peter has.
A quick look at the table of contents suggests that it's focused more on model-agnostic methods. I think you'd get a different overview of the field compared to the papers we've summarized here, as an fyi.
I think one large area you'd miss out on from reading the book is the recent work on making neural nets more interpretable, or designing more interpretable neural net architectures (e.g. NBDT).
Thanks! Didn't realize we had a double entry, will go and edit.
For even higher variance crypto:
In case you haven't seen, similar projects exist:
See also previous discussion here