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New streaming algorithm enhances robust max-min diversification

By PulseAugur Editorial · [1 sources] ·

This paper introduces a new streaming algorithm for robust max-min diversification, addressing limitations in a previous formulation by Amagata (AAAI23). The proposed algorithm offers a deterministic approach that guarantees exactly k inliers and provides a $(2+\varepsilon)$-approximate solution, even without outliers. It utilizes memory independent of the total number of points in the stream and achieves an amortized update time also independent of the stream size for a wide range of parameters. AI

IMPACT Improves efficiency and robustness of data diversification algorithms, potentially impacting downstream ML applications.

RANK_REASON Academic paper detailing a new algorithm for a specific 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 streaming algorithm enhances robust max-min diversification

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Academic paper detailing a new algorithm for a specific 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) · Andrea Pietracaprina, Geppino Pucci, Stefano Zanon ·

    Streaming algorithms for robust max-min diversification

    arXiv:2610.01456v1 Announce Type: new Abstract: Given a set of $n$ points $X$ in a metric space and an integer $k$, max-min diversification aims to select $k$ points of $X$ maximizing their minimum pairwise distance. This objective function is however highly vulnerable to noisy p…

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