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New algorithm learns halfspaces without synthetic data

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Researchers have developed a new algorithm for learning halfspaces without relying on synthetic data, addressing a long-standing challenge in computational geometry. The algorithm achieves tight bounds of $\Theta(D + \log n)$ for learning halfspaces with normal vectors from a set of size $D$. This approach also yields nearly optimal algorithms for PAC-learning, requiring $O(\min(D + \log(1/\varepsilon), 1/\varepsilon) \cdot \log D)$ queries to learn a function within error $\varepsilon$, even with adversarial corruption. AI

RANK_REASON The cluster contains an academic paper detailing a new algorithm for a computational geometry problem. [lever_c_demoted from research: ic=1 ai=0.4]

Read on arXiv cs.LG →

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New algorithm learns halfspaces without synthetic data

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The cluster contains an academic paper detailing a new algorithm for a computational geometry problem. [lever_c_demoted from research: ic=1 ai=0.4]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Hadley Black, Kasper Green Larsen, Arya Mazumdar, Barna Saha, Geelon So ·

    Actively Learning Halfspaces without Synthetic Data

    arXiv:2509.20848v2 Announce Type: replace-cross Abstract: In the classic point location problem, one is given an arbitrary dataset $X \subset \mathbb{R}^d$ of $n$ points with query access to an unknown halfspace $f : \mathbb{R}^d \to \{0,1\}$, and the goal is to learn the label o…

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