sists in selecting the next point(s) to evaluate in a way that manages the trade-off between exploitation of candidates with high predicted performance and exploration of regions with high uncertainty. In the GPEI model, a Matern 5/2 kernel is used by default which allows for somewhat less smooth behavior than the radial basis function. Parameters are mapped to a range between 0 and 1 and the objective values are standardized (subtract mean, divide by standard deviation) per default transformation behavior within Ax, and parameter constraints are imposed as hard constraints. With BoTorch as the backend for Ax, Ax leverages auto-differentiation to perform gradient-enhanced optimization of acquisition functions. Finally, maximum a-posteriori estimation is used on the marginal log likelihood during acquisition function optimization. For additional details, please see Balandat et al. .