I want to learn parameter preferences of users for different algorithms. The users are queried for their preference for one of the visualizations generated from a pair of parameter configurations for an algorithm.
So I have some data, that contain a lot of tuples like $(X, Y, D)$, where $X$ and $Y$ are parameter configurations and $D = 1$, if $X$ is preferred to $Y$, otherwise $D = 0$.
From this data, I want to be somehow able to apply Bayesian optimization with Gaussian processes.
Does some implementation for that already exist?