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Highly Adaptive Ridge regression method introduced for tabular data

A new regression method called Highly Adaptive Ridge (HAR) has been proposed, achieving a dimension-free convergence rate of n^{-1/3} for right-continuous functions with square-integrable sectional derivatives. This method is particularly suited for tabular data and is computationally efficient, functioning as kernel ridge regression with a specific data-adaptive kernel. The research also establishes connections between HAR and kernel ridge regression in Sobolev spaces and validates the theory through simulations and real-world data. AI

IMPACT Introduces a novel regression technique potentially improving machine learning model performance on tabular data.

RANK_REASON Academic paper introducing a new statistical method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Highly Adaptive Ridge regression method introduced for tabular data

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Academic paper introducing a new statistical method. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Alejandro Schuler, Mingxun Wang, Alexander Hagemeister, Mark van der Laan ·

    Highly Adaptive Ridge

    arXiv:2410.02680v2 Announce Type: replace Abstract: In this paper we propose the Highly Adaptive Ridge (HAR): a regression method that achieves an $n^{-1/3}$ dimension-free $\mathscr L_2$ convergence rate (up to log factors) in the class of right-continuous functions with square-…