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New papers explore scalable semi-supervised learning with graph cuts and sparsification

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 4 sources

Two new research papers introduce novel algorithms for semi-supervised learning (SSL). One paper presents Sparse-HFS, an algorithm designed for large-scale SSL problems that significantly reduces space and time complexity. The other paper proposes a max-margin graph cut method that aims to outperform existing state-of-the-art approaches like manifold regularization of support vector machines. AI

Summary written by gemini-2.5-flash-lite from 4 sources. How we write summaries →

IMPACT Introduces new algorithmic approaches for semi-supervised learning, potentially improving efficiency and performance on complex datasets.

RANK_REASON Two new academic papers on arXiv present novel algorithms for semi-supervised learning.

Read on arXiv cs.LG →

COVERAGE [4]

  1. arXiv cs.LG TIER_1 · Daniele Calandriello, Alessandro Lazaric, Michal Valko ·

    Large-scale semi-supervised learning with online spectral graph sparsification

    arXiv:2604.26550v1 Announce Type: new Abstract: We introduce Sparse-HFS, a scalable algorithm that can compute solutions to SSL problems using only O(n polylog(n)) space and O(m polylog(n)) time.

  2. arXiv cs.LG TIER_1 · Branislav Kveton, Michal Valko, Ali Rahimi, Ling Huang ·

    Semi-supervised learning with max-margin graph cuts

    arXiv:2604.26818v1 Announce Type: new Abstract: This paper proposes a novel algorithm for semisupervised learning. This algorithm learns graph cuts that maximize the margin with respect to the labels induced by the harmonic function solution. We motivate the approach, compare it …

  3. arXiv cs.LG TIER_1 · Ling Huang ·

    Semi-supervised learning with max-margin graph cuts

    This paper proposes a novel algorithm for semisupervised learning. This algorithm learns graph cuts that maximize the margin with respect to the labels induced by the harmonic function solution. We motivate the approach, compare it to existing work, and prove a bound on its gener…

  4. arXiv cs.LG TIER_1 · Michal Valko ·

    Large-scale semi-supervised learning with online spectral graph sparsification

    We introduce Sparse-HFS, a scalable algorithm that can compute solutions to SSL problems using only O(n polylog(n)) space and O(m polylog(n)) time.

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