Vectorized Adaptive Histograms for Sparse Oblique Forests
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arXiv
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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866912932105814016 |
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| author | Lubonja, Ariel Yoon, Jungsang Xu, Haoyin Wan, Yue Xu, Yilin Stotz, Richard Guillame-Bert, Mathieu Vogelstein, Joshua T. Burns, Randal |
| author_facet | Lubonja, Ariel Yoon, Jungsang Xu, Haoyin Wan, Yue Xu, Yilin Stotz, Richard Guillame-Bert, Mathieu Vogelstein, Joshua T. Burns, Randal |
| contents | Classification using sparse oblique random forests provides guarantees on uncertainty and confidence while controlling for specific error types. However, they use more data and more compute than other tree ensembles because they create deep trees and need to sort or histogram linear combinations of data at runtime. We provide a method for dynamically switching between histograms and sorting to find the best split. We further optimize histogram construction using vector intrinsics. Evaluating this on large datasets, our optimizations speedup training by 1.7-2.5x compared to existing oblique forests and 1.5-2x compared to standard random forests. We also provide a GPU and hybrid CPU-GPU implementation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_00326 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Vectorized Adaptive Histograms for Sparse Oblique Forests Lubonja, Ariel Yoon, Jungsang Xu, Haoyin Wan, Yue Xu, Yilin Stotz, Richard Guillame-Bert, Mathieu Vogelstein, Joshua T. Burns, Randal Machine Learning Distributed, Parallel, and Cluster Computing Performance I.2.6; D.1.3 Classification using sparse oblique random forests provides guarantees on uncertainty and confidence while controlling for specific error types. However, they use more data and more compute than other tree ensembles because they create deep trees and need to sort or histogram linear combinations of data at runtime. We provide a method for dynamically switching between histograms and sorting to find the best split. We further optimize histogram construction using vector intrinsics. Evaluating this on large datasets, our optimizations speedup training by 1.7-2.5x compared to existing oblique forests and 1.5-2x compared to standard random forests. We also provide a GPU and hybrid CPU-GPU implementation. |
| title | Vectorized Adaptive Histograms for Sparse Oblique Forests |
| topic | Machine Learning Distributed, Parallel, and Cluster Computing Performance I.2.6; D.1.3 |
| url | https://arxiv.org/abs/2603.00326 |