mlsanityData to 7 Oct 2026

Improved Inapproximability of VC Dimension and Littlestone's Dimension via (Unbalanced) Biclique

arXiv 2211.01443 ↗First posted 2 Nov 2022cs.CC cs.LGcited by 7

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Abstract (arXiv)

We study the complexity of computing (and approximating) VC Dimension and Littlestone's Dimension when we are given the concept class explicitly. We give a simple reduction from Maximum (Unbalanced) Biclique problem to approximating VC Dimension and Littlestone's Dimension. With this connection, we derive a range of hardness of approximation results and running time lower bounds. For example, under the (randomized) Gap-Exponential Time Hypothesis or the Strongish Planted Clique Hypothesis, we show a tight inapproximability result: both dimensions are hard to approximate to within a factor of o(log n) in polynomial-time. These improve upon constant-factor inapproximability results from [Manurangsi and Rubinstein, COLT 2017].

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 3 of its 43 references that are in the map. First posted on arXiv 2 Nov 2022.

Builds on

3 of 43 references.

References · 3

  1. 2015The Optimal Sample Complexity of PAC Learningcited by 72
  2. 2020A Survey on Approximation in Parameterized Complexity: Hardness and Algorithmscited by 5
  3. 2020The Strongish Planted Clique Hypothesis and Its Consequencescited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 1; 2025: 2; 2026: 1.

7 papers.