Work

Learning Functions of Halfspaces

Year: 2026

Type: preprint

Abstract: We give an algorithm that learns arbitrary Boolean functions of $k$ arbitrary halfspaces over $\mathbb{R}^n$, in the challenging distribution-free Probably Approximately Correct (PAC) learning model, running in time $2^{\sqrt{n} \cdot (\log n)^{O(k)}}$. This is the first algorithm that can PAC learn even intersections of two halfspaces in time $2^{o(n)}.$

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2603.08700

Authors: Alman, Josh, Patel, Shyamal, Servedio, Rocco A.

Institution: Columbia University

Topic: Machine Learning and Algorithms

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W7134809681

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