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Evidence record 4721 · automatically gathered

Approximation-Free Differentiable Oblique Decision Trees

Decision Trees (DTs) are widely used in safety-critical domains such as medical diagnosis, valued for their interpretability and effectiveness on tabular data. However, training accurate oblique DTs is challenging due to complex optimization landscapes and overfitting risks, particularly in regression. Recent advances have introduced differentiable formulations that enable gradient-based training and joint optimization of decision boundaries and leaf regressors. Yet, existing approaches typicall

Record details

Published: 8 May 2026
Source: arXiv
Category: Research
Topics: Safety & alignment · Healthcare
Retrieved: 14 July 2026

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How to cite this record

ethics.ai (8 May 2026), “Approximation-Free Differentiable Oblique Decision Trees,” evidence record 4721, https://ethics.ai/record/4721 (originally published by arXiv).

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