New algorithm advances active regression for single-index models

This paper introduces a novel active regression algorithm for single-index models with unknown link functions and general $\ell_p$-loss. The proposed non-adaptive sampling algorithm achieves a $(1+ε)$-approximation with a specific query complexity, addressing a more challenging setting than prior work. The research also establishes nearly tight lower bounds for $p>2$, significantly closing the gap in active $\ell_p$-regression for these models. AI

IMPACT Advances theoretical understanding and algorithmic approaches for single-index models, potentially improving performance in applications requiring regression with unknown link functions.

RANK_REASON The cluster contains a research paper detailing a new algorithm and theoretical bounds for a specific machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Hugging Face Daily Papers →

• Active Regression by Stratification
• $\\ell_p$-loss
• Hugging Face
• Lipschitz
• single-index models

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