The proposed implementation of Gunsilius' algorithm computes For example, in the expenditure dataset (see Section I.3), In Figure 4, we show the results of Gunsilius's algorithm for three different Note that this algorithm works on the empirical CDFs of all variables, i.e., they are all scaled to lie Figure 4: We show results of Gunsilius's algorithm for 3 different settings of The practical issue of course is the optimization. That alone is already very computationally demanding and has convergence problems. A practical resource, sample size, limits the representational size of the estimator. How to achieve "enough variability" without aiming at a completely flexible distribution of In any case, the finite mixture of Gaussians approach can still be implemented with the reparameter-ization trick. The relation to Gunsilius algorithm is that our "base measure" is smoothly adaptive, leading to possibly more stable behavior in practice.

Wethus establish that policy iteration on reward-robust MDPs can have the same time complexityasonregularizedMDPs.

Weprovide asemi-supervised estimation procedure of the optimal rule involving two datasets: a firstlabeled dataset is used to estimate both regression function and conditional variance function while a secondunlabeleddataset is exploited to calibrate the desired rejection rate.