mlsanityData to 7 Oct 2026

Consistency of semi-supervised learning, stochastic tug-of-war games, and the p-Laplacian

arXiv 2401.07463 ↗First posted 15 Jan 2024math.ST cs.LG cs.NAcited by 3

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Abstract (arXiv)

In this paper we give a broad overview of the intersection of partial differential equations (PDEs) and graph-based semi-supervised learning. The overview is focused on a large body of recent work on PDE continuum limits of graph-based learning, which have been used to prove well-posedness of semi-supervised learning algorithms in the large data limit. We highlight some interesting research directions revolving around consistency of graph-based semi-supervised learning, and present some new results on the consistency of p-Laplacian semi-supervised learning using the stochastic tug-of-war game interpretation of the p-Laplacian. We also present the results of some numerical experiments that illustrate our results and suggest directions for future work.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 34 of its 122 references that are in the map. First posted on arXiv 15 Jan 2024.

Builds on

34 of 122 references.

References · 34

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22 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2024Numerical solution of a PDE arising from prediction with expert advicecited by 2
  2. 2025Predicting Anthropometric Body Composition Variables Using 3D Optical Imaging and Machine Learningcited by 1
  3. 2026Target-Aware State-Adaptive p-Dirichlet Graph Neural Regression for Non-Invasive Body-Composition Estimationcited by 0