Probabilistic prediction aims to compute predictive distributions rather than single point predictions. These distributions enable practitioners to quantify uncertainty, compute risk, and detect outliers. However, most probabilistic methods assume parametric responses, such as Gaussian or Poisson distributions.

's are real-valued weights depending on the accuracy of h

In particular, interpreting Random Forests (RFs) [2] and its variants [14, 28, 27, 29, 1, 12] has become an important area of research due to the wide ranging applications of RFs invarious scientific areas, such asgenome-wide association studies (GWAS)[7],gene expression microarray[13,23],andgeneregulatorynetworks[9].

Thank you for all the reviewers time and effort. Thank you for your detailed review. Here, the idea is to re-train our model when new data is available. Here we explain our design space (see additional details in Appendix A.3, B and C); (i) Choice of embedding (joint vs Reviewer 3 Thank you for your review, and for comments regarding experiments, please see above. Thank you for your positive comments regarding the quality of the paper.

Neural Information Processing Systems http://nips.cc/