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Soft Gradient Boosting With Learnable Feature Transforms for Sequential Regression
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DOI：10.1109/LSP.2025.3639345.png)
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Abstract
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We propose a soft gradient boosting framework for sequential regression that embeds a learnable linear feature transform within the boosting procedure. At each boosting iteration, we train a soft decision tree and learn a linear input feature transform $\mathbf {Q}$ together. This approach is particularly advantageous in high-dimensional, data-scarce scenarios, as it discovers the most relevant input representations while boosting.We demonstrate, using both synthetic and real-world datasets, that our method effectively and efficiently increases the performance by an end-to-end optimization of feature selection/transform and boosting while avoiding overfitting. We also extend our algorithm to differentiable non-linear transforms if overfitting is not a problem. To support reproducibility and future work, we share our code publicly.
Keywords:
Gradient boosting
sequential regression
soft decision trees
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3.9
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630
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Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
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Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data MiningIF0
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