31–40 of 86 posts
Okay, this is awesome (and easy to miss with a just a cursory skim): "5.3 Delicious Chocolate Chip Cookies Vizier is also used to solve complex black–box optimization problems arising from physical design or logistical problems. Here we present an example that highlights some additional capabilities of the system: finding the most delicious chocolate chip cookie recipe from a parameterized space of recipes... We prov…
Okay, this is awesome (and easy to miss with a just a cursory skim): "5.3 Delicious Chocolate Chip Cookies Vizier is also used to solve complex black–box optimization problems arising from physical design or logistical problems. Here we present an example that highlights some additional capabilities of the system: finding the most delicious chocolate chip cookie recipe from a parameterized space of recipes... We prov…
I worked for Google at the time they went through the cookie iterations in the cafes. Personally I disliked all the cookies that ever came out of this project, mainly because they contained spices, I heard the same thing from many others. After you got handed a cookie you were asked to fill out a feedback form on a tablet, which almost always is annoying. But more importantly, the process of optimizing the ingredient…
Earlier quoted context omitted.
Did they post the recipe?
They didn't. I am very disappointed. I, for one, welcome our new robot overlords and also want to try their chocolate chip cookies.
Only up to 64 variables? Why such small problems? And why are their results are averaged across all of the different test functions? I'd like to see the performance difference between Rosenbrock and Rastrigin, thank you very much. And they have a weird fixation on stopping rules, when in general your stopping rule is how many evaluations you can afford. Was this written by interns? There's no discussion of the retrog…
It is 64 hyper-parameters, not variables, which is a huge amount. Stopping rules are also a requirement of tuning. Can't just have a while True:. If an objective function doesn't improve after 100 (or any #), there needs to be logic to stop the trials since this system is apparently serving all of Alphabet.
Earlier quoted context omitted.
It is 64 hyper-parameters, not variables, which is a huge amount. Stopping rules are also a requirement of tuning. Can't just have a while True:. If an objective function doesn't improve after 100 (or any #), there needs to be logic to stop the trials since this system is apparently serving all of Alphabet.
I think that in the context that he meant, 64 variables = 64 hyperparameters because it was about benchmark problems solved in black-box settings. In other words, it is not about the common misconception of comparing #hyperparameters and #variables on some ML model, where the latter can be in 10^7.
Okay, this is awesome (and easy to miss with a just a cursory skim): "5.3 Delicious Chocolate Chip Cookies Vizier is also used to solve complex black–box optimization problems arising from physical design or logistical problems. Here we present an example that highlights some additional capabilities of the system: finding the most delicious chocolate chip cookie recipe from a parameterized space of recipes... We prov…
Did they post the recipe?
Earlier quoted context omitted.
Here you go: https://www.blog.google/technology/research/makings-smart-co...
These are not the vizier cookies.
Okay, this is awesome (and easy to miss with a just a cursory skim): "5.3 Delicious Chocolate Chip Cookies Vizier is also used to solve complex black–box optimization problems arising from physical design or logistical problems. Here we present an example that highlights some additional capabilities of the system: finding the most delicious chocolate chip cookie recipe from a parameterized space of recipes... We prov…
It's great that they published this - I wasn't sure if I could share this externally. I see they left the ingredient that really improved the scores unexpectedly out, presumbly for proprietary reasons.
Only up to 64 variables? Why such small problems? And why are their results are averaged across all of the different test functions? I'd like to see the performance difference between Rosenbrock and Rastrigin, thank you very much. And they have a weird fixation on stopping rules, when in general your stopping rule is how many evaluations you can afford. Was this written by interns? There's no discussion of the retrog…
How long it will take CMAES to solve 10-dimensional Rosenbrock? Here, it takes about 700 function evaluations: https://indiesolver.com/tutorials/rosenbrock_python Please note that their work is primarily for expensive optimization where you cannot afford millions of function evaluations like you would need on rotated Rastrigin to solve it exactly for n>64.
It really depends on parameterization though. Did they use off-the-shelf parameters? Did they make any attempt to tune CMAES for their test functions? Did they make any attempt to tune their own optimizer for the test functions? It's really easy to put your thumb on the scale when you're writing a paper about a black-box optimizer, and my default reaction is to assume that any paper claiming to equal or outdo CMAES is pure B.S. This paper did nothing to convince me otherwise.