Higher-arity PAC learning, VC dimension and packing lemma
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Abstract (arXiv)
The aim of this note is to overview some of our work in Chernikov, Towsner'20 (arXiv:2010.00726) developing higher arity VC theory (VCn dimension), including a generalization of Haussler packing lemma, and an associated tame (slice-wise) hypergraph regularity lemma; and to demonstrate that it characterizes higher arity PAC learning (PACn learning) in n-fold product spaces with respect to product measures introduced by Kobayashi, Kuriyama and Takeuchi'15. We also point out how some of the recent results in arXiv:2402.14294, arXiv:2505.15688, arXiv:2509.20404 follow from our work in arXiv:2010.00726.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 3 of its 24 references that are in the map. First posted on arXiv 2 Oct 2025.
Builds on
3 of 24 references.
References · 3
- 2024High-arity PAC learning via exchangeabilitycited by 4
- 2025From learnable objects to learnable random objectscited by 4
- 2025Sample completion, structured correlation, and Netflix problemscited by 2
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Citing papers per year: 2025: 1.
1 paper.
- 2025Remarks on a recent preprint of Chernikov and Towsnercited by 0
- 2025Remarks on a recent preprint of Chernikov and Towsner22 Oct 2025