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Found 6 papers, 4 papers with code
Simplest Streaming Trees
Nonetheless, we found that those state-of-the-art algorithms suffer from a number of drawbacks, including performing very poorly on some problems and requiring a huge amount of memory on others.
When are Deep Networks really better than Decision Forests at small sample sizes, and how?
Empirically, we compare these two strategies on hundreds of tabular data settings, as well as several vision and auditory settings.
The Chi-Square Test of Distance Correlation
One major bottleneck is the testing process: because the null distribution of distance correlation depends on the underlying random variables and metric choice, it typically requires a permutation test to estimate the null and compute the p-value, which is very costly for large amount of data.
Nonpar MANOVA via Independence Testing
The $k$-sample testing problem tests whether or not $k$ groups of data points are sampled from the same distribution.
hyppo: A Multivariate Hypothesis Testing Python Package
We introduce hyppo, a unified library for performing multivariate hypothesis testing, including independence, two-sample, and k-sample testing.
Learning Interpretable Characteristic Kernels via Decision Forests
It has been demonstrated that these proximity matrices can be thought of as kernels, connecting the decision forest literature to the extensive kernel machine literature.
