16 citations · 21 across the 10 of their papers we have counts for
10 papers
Vectorized Dynamic Histograms for Sparse Oblique Forests
Ariel Lubonja, Jungsang Yoon, Haoyin Xu +6
Sparse oblique (SPO), part of the top-ranked configuration of Google's Yggdrasil Decision Forests (YDF), improve the accuracy while maintaining interpretability of Random Forests (…
Boosting gets full Attention for Relational Learning
Mathieu Guillame-Bert, Richard Nock
More often than not in benchmark supervised ML, tabular data is flat, i.e. consists of a single (rows, columns) file, but cases abound in the real world where observat…
Generative Forests
Richard Nock, Mathieu Guillame-Bert
We focus on generative AI for a type of data that still represent one of the most prevalent form of data: tabular data. Our paper introduces two key contributions: a new powerful c…
Yggdrasil Decision Forests: A Fast and Extensible Decision Forests Library
Mathieu Guillame-Bert, Sebastian Bruch, Richard Stotz +1
Yggdrasil Decision Forests is a library for the training, serving and interpretation of decision forest models, targeted both at research and production work, implemented in C++, a…
Generative Trees: Adversarial and Copycat
Richard Nock, Mathieu Guillame-Bert
While Generative Adversarial Networks (GANs) achieve spectacular results on unstructured data like images, there is still a gap on tabular data, data for which state of the art sup…
Modeling Text with Decision Forests using Categorical-Set Splits
Mathieu Guillame-Bert, Sebastian Bruch, Petr Mitrichev +2
Decision forest algorithms typically model data by learning a binary tree structure recursively where every node splits the feature space into two sub-regions, sending examples int…