5 citations · 5 across the 1 of their papers we have counts for
3 papers
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…
Interpretable Learning-to-Rank with Generalized Additive Models
Honglei Zhuang, Xuanhui Wang, Michael Bendersky +7
Interpretability of learning-to-rank models is a crucial yet relatively under-examined research area. Recent progress on interpretable ranking models largely focuses on generating…
TF Boosted Trees: A scalable TensorFlow based framework for gradient boosting
Natalia Ponomareva, Soroush Radpour, Gilbert Hendry +4
TF Boosted Trees (TFBT) is a new open-sourced frame-work for the distributed training of gradient boosted trees. It is based on TensorFlow, and its distinguishing features include…