Decision Tree Learning
Figure 1: Protein with random forest across 140 evaluations with different NN structure for distGP's
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.
Minimal Variance Sampling in Stochastic Gradient Boosting
Neural Information Processing Systems http://nips.cc/
Estimating decision tree learnability with polylogarithmic sample complexity
We show that top-down decision tree learning heuristics (such as ID3, C4.5, and
Estimating decision tree learnability with polylogarithmic sample complexity
We show that top-down decision tree learning heuristics (such as ID3, C4.5, and
Export Reviews, Discussions, Author Feedback and Meta-Reviews
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.
Mondrian Forests: Efficient Online Random Forests
Balaji Lakshminarayanan, Daniel M. Roy, Yee Whye Teh
Neural Information Processing Systems http://nips.cc/
Logarithmic Time Online Multiclass prediction
Anna E. Choromanska, John Langford
Neural Information Processing Systems http://nips.cc/
Provably robust boosted decision stumps and trees against adversarial attacks
Maksym Andriushchenko, Matthias Hein
Neural Information Processing Systems http://nips.cc/
Supplementary Material for Classification with Valid and Adaptive Coverage Y aniv Romano
Here, we consider the jackknife+--i.e., Algorithm S1 describes the extension of Algorithm 1 discussed in Section 2.5, which ensures The validity of this algorithm is established by the following result. We begin by proving the lower bound on coverage. This will become apparent after we reduce our claim to the setting in the aforementioned paper. This is easy to verify. Let σ (1),...,σ ( n + m) be the permutation of the data points corresponding to Σ, so that (ΣA Σ S3.1 Implementation details We have applied the following black-box classification methods to estimate label probabilities: JK+ is omitted for computational reasons. The performances of the different methods on data generated from this model are compared in Figure S3.