2015 Optimization Testing Uncertainty 
The Unofficial Google Data Science Blog
APRIL 23, 2024
If the relationship of $X$ to $Y$ can be approximated as quadratic (or any polynomial), the objective and constraints as linear in $Y$, then there is a way to express the optimization as a quadratically constrained quadratic program (QCQP). Crucially, it takes into account the uncertainty inherent in our experiments.
datapine
SEPTEMBER 4, 2023
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The Unofficial Google Data Science Blog
JULY 22, 2020
Another reason to use ramp-up is to test if a website's infrastructure can handle deploying a new arm to all of its users. The website wants to make sure they have the infrastructure to handle the feature while testing if engagement increases enough to justify the infrastructure. We offer two examples where this may be the case.
The Unofficial Google Data Science Blog
MARCH 31, 2016
In the context of prediction problems, another benefit is that the models produce an estimate of the uncertainty in their predictions: the predictive posterior distribution. both L1 and L2 penalties; see [8]) which were tuned for test set accuracy (log likelihood). arXiv preprint arXiv:1506.04416 (2015). [6] bandit problems).
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