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New Bounds Established for Online Sparse Linear Regression Regret

By PulseAugur Editorial · [1 sources] ·

Researchers have established new theoretical bounds for online sparse linear regression, a complex problem where algorithms select a limited number of attributes for prediction. This work introduces the first lower bound on the minimax regret for this task and proposes algorithms that achieve improved upper bounds, even without regularity assumptions. The findings provide a clearer understanding of the information-theoretic complexity involved in online sparse linear regression. AI

IMPACT Provides theoretical groundwork for understanding the complexity of online learning algorithms.

RANK_REASON Academic paper detailing new theoretical bounds for a machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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

New Bounds Established for Online Sparse Linear Regression Regret

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Academic paper detailing new theoretical bounds for a machine learning problem. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Xiaofeng Cao, Junfan Li, Langzhang Liang, Mingwei Xu, Xiao Zhang ·

    New Lower Bound and Upper Bounds on the Regret for Online Sparse Linear Regression

    arXiv:2610.11551v1 Announce Type: new Abstract: We study online sparse linear regression (OSLR) where any algorithm is restricted to accessing only $b$ out of $d$ attributes per instance for prediction and $b_0\geq 0$ additional attributes after prediction, which was proved to be…

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