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New Hinge Regression Trees offer compact tabular model learning

By PulseAugur Editorial · [2 sources] ·

Researchers have developed Hinge Regression Trees (HRT) and HRT-Boost, a new framework for creating compact tabular models. This approach reframes oblique split optimization as a nonlinear least-squares problem, allowing for efficient node-level optimization. The HRT-Boost ensemble method further enhances performance with stage-wise functional gradient descent, showing competitive results against existing baselines and producing smaller models. AI

IMPACT Introduces a novel method for building more compact and efficient tabular models, potentially improving performance in data analysis tasks.

RANK_REASON The cluster contains a research paper detailing a new machine learning framework and its ensemble extension.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Hongyi Li, Jun Xu, Hong Yan ·

    Hinge Regression Trees and HRT-Boost: Newton-Optimized Oblique Learning for Compact Tabular Models

    arXiv:2605.23422v1 Announce Type: new Abstract: Learning high-quality oblique decision trees remains a significant challenge due to the discrete and non-convex nature of split optimization. We present the Hinge Regression Tree (HRT) framework, which reframes each oblique split as…

  2. arXiv cs.LG TIER_1 English(EN) · Hong Yan ·

    Hinge Regression Trees and HRT-Boost: Newton-Optimized Oblique Learning for Compact Tabular Models

    Learning high-quality oblique decision trees remains a significant challenge due to the discrete and non-convex nature of split optimization. We present the Hinge Regression Tree (HRT) framework, which reframes each oblique split as a nonlinear least-squares problem over two line…

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