Soft Gradient Boosting with Learnable Feature Transforms for Sequential Regression
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866912589690175488 |
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| author | Karaca, Huseyin Kozat, Suleyman Serdar |
| author_facet | Karaca, Huseyin Kozat, Suleyman Serdar |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_12920 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Soft Gradient Boosting with Learnable Feature Transforms for Sequential Regression Karaca, Huseyin Kozat, Suleyman Serdar Machine Learning Signal Processing 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. |
| title | Soft Gradient Boosting with Learnable Feature Transforms for Sequential Regression |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2509.12920 |