Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classes
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Abstract (arXiv)
In this work we study the quantitative relation between the recursive teaching dimension (RTD) and the VC dimension (VCD) of concept classes of finite sizes. The RTD of a concept class mathcal C subseteq 0, 1n, introduced by Zilles et al. (2011), is a combinatorial complexity measure characterized by the worst-case number of examples necessary to identify a concept in mathcal C according to the recursive teaching model. For any finite concept class mathcal C subseteq 0,1n with VCD(mathcal C)=d, Simon & Zilles (2015) posed an open problem RTD(mathcal C) = O(d), i.e., is RTD linearly upper bounded by VCD? Previously, the best known result is an exponential upper bound RTD(mathcal C) = O(d · 2d), due to Chen et al. (2016). In this paper, we show a quadratic upper bound: RTD(mathcal C) = O(d2), much closer to an answer to the open problem. We also discuss the challenges in fully solving the problem.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Feb 2017.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 2; 2026: 1.
6 papers.
- 2023Non-Clashing Teaching Maps for Balls in Graphscited by 5
- 2025The Parameterized Complexity of Computing the VC-Dimensioncited by 1
- 2022A Labelled Sample Compression Scheme of Size at Most Quadratic in the VC Dimensioncited by 0
- 2023A Note on Hardness of Computing Recursive Teaching Dimensioncited by 0
- 2025Lower Bounds for Greedy Teaching Set Constructionscited by 0
- 2026Certification from Examples is Hard for Circuits and Transformers under Minimal Overparametrizationcited by 0
- 2026Certification from Examples is Hard for Circuits and Transformers under Minimal Overparametrization21 May 2026
- 2025The Parameterized Complexity of Computing the VC-Dimension20 Oct 2025
- 2025Lower Bounds for Greedy Teaching Set Constructions6 May 2025
- 2023Non-Clashing Teaching Maps for Balls in Graphs6 Sep 2023
- 2023A Note on Hardness of Computing Recursive Teaching Dimension19 Jul 2023
- 2022A Labelled Sample Compression Scheme of Size at Most Quadratic in the VC Dimension24 Dec 2022