Revisiting default parameter settings?

Hello all,

I came upon a [recent JMLR paper](http://jmlr.org/papers/volume20/18-444/18-444.pdf) that examined the "tunability" of the hyperparameters of multiple algorithms, including XGBoost.

Their methodology, as far as I understand it, is to take the default parameters of the package, find the (near) optimal parameters for each dataset in their evaluation and determine how valuable it is to tune a particular parameter.

In doing so they also come up with "optimal defaults" in Table 3, and an interactive [Shiny app](https://philipppro.shinyapps.io/tunability/).

This made me curious about how the defaults for XGBoost were chosen and if it's something that the community would be interested in revisiting in the future.