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New algorithm advances active regression for single-index models

By PulseAugur Editorial · [1 sources] ·

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 →

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

New algorithm advances active regression for single-index models

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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]
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COVERAGE [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    Active Regression for Single-Index Models with Unknown Link Functions

    This paper studies active regression for single-index models under general $\ell_p$-loss with an unknown $1$-Lipschitz link function $f$, formulated as $\min_{f,x} \|f(Ax)-b\|_p^p$ with full access to $A$ but coordinate-query access to $b$. Prior work established upper bounds for…

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