It's a little strange that they do not have a track that gives gradient information, given that it is often a real world possibility. Also, this basically allows unlimited time between eval... So this becomes a contest about - coming up with a distribution over R^n -> R function - finding the optimal evaluation points to do Bayesian update

I predict the winner will use some a mixture of Gaussian processes with various kernels and stochastic control (with a limited look ahead, otherwise it blows up) to pick the test points.