Decision trees are a fundamental workhorse in machine learning. Their logical and hierarchical structure makes them easy to understand and their predictions easy to explain.

Points were sampled uniformly in the bands denoted by dashed lines. We posit that these barriers are due, at least in part, to the sensitivity of decision trees to transformations of the input resulting from greedy construction and simple decision rules. Of these, key limitation is the latter; even if we replace greedy construction with a perfect tree learner, simple distributions can nonetheless require an arbitrarily large axis-aligned tree to fit.

This work takes one step towards data encryption by incorporating some crucial ingredients of learning algorithm.

On one hand, the knowledge learned from public data is implicitly transferred into the private model.

's weights define an exponential family.

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.