Adrian Price-Whelan (Flatiron) and I are working on data-driven distances for stars in the SDSS-IV APOGEE data. There are many hyper-parameters of our method, including the number K of leave one-Kth-out splits of the data, the regularization amplitude we apply to the spectral part of the model (it's a generalized linear model), and the infamous Gaia parallax zero-point. These are just three of many, but they span an interesting range. One is purely operational, one restricts the fit (introduces bias, deliberately), and one has a true value that is unknown. How to optimize for each of these? It will be different in each case, I expect.
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