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/

We show that top-down decision tree learning heuristics (such as ID3, C4.5, and

We show that top-down decision tree learning heuristics (such as ID3, C4.5, and

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper describes a Bayesian model for online learning in the context of random forests models for supervised classification. The main contribution of the paper is the formulation of a novel prior on binary rooted trees that relies on the Mondrian process. An additional novelty of the paper is the use of hierarchical normalized stable processes as priors for the probabilities of the different classes at each terminal node. The paper is well written and the formulation novel.

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

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

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