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 of every point in $X$. This problem is extremely well-studied and a nearly-optimal $\widetilde{O}(d \log n)$ query algorithm is known due to Hopkins-Kane-Lovett-Mahajan (FOCS 2020). However, their algorithm is granted the power to query arbitrary points outside of $X$ (point synthesis), and in fact without this power th
This paper addresses a known limitation in active learning for halfspaces, proposing a method to achieve similar efficiency without relying on synthetic data, a constraint often present in real-world applications.
Improving active learning efficiency without synthetic data broadens the applicability of AI in scenarios where data generation is difficult or impossible, making AI more practical for diverse datasets.
The ability to learn complex decision boundaries more efficiently from existing datasets without requiring additional synthetic queries reduces the cost and complexity of deploying certain AI models.
- · AI researchers
- · Data-constrained industries
- · Machine learning platform providers
- · AI models reliant on synthetic data
More efficient active learning algorithms will emerge that are less reliant on generating new data points.
This could lead to a reduction in the need for human data annotators in some specialized active learning workflows.
The development of these algorithms might accelerate the adoption of AI in fields with sensitive or limited data availability, such as healthcare or finance.
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Read at arXiv cs.LG