Soft Gradient Boosting with Learnable Feature Transforms for Sequential Regression

Huseyin Karaca; Suleyman Serdar Kozat

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arXiv · 2509.12920

Soft Gradient Boosting with Learnable Feature Transforms for Sequential Regression

Huseyin Karaca·Suleyman Serdar Kozat

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Abstract

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 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.

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Huseyin Karaca, Suleyman Serdar Kozat. 2025-09-16. Soft Gradient Boosting with Learnable Feature Transforms for Sequential Regression. https://arxiv.org/abs/2509.12920

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