A Labelled Sample Compression Scheme of Size at Most Quadratic in the VC Dimension
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Abstract (arXiv)
This paper presents a construction of a proper and stable labelled sample compression scheme of size O(VCD2) for any finite concept class, where VCD denotes the Vapnik-Chervonenkis Dimension. The construction is based on a well-known model of machine teaching, referred to as recursive teaching dimension. This substantially improves on the currently best known bound on the size of sample compression schemes (due to Moran and Yehudayoff), which is exponential in VCD. The long-standing open question whether the smallest size of a sample compression scheme is in O(VCD) remains unresolved, but our results show that research on machine teaching is a promising avenue for the study of this open problem. As further evidence of the strong connections between machine teaching and sample compression, we prove that the model of no-clash teaching, introduced by Kirkpatrick et al., can be used to define a non-trivial lower bound on the size of stable sample compression schemes.
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References · 6
- 2020Proper Learning, Helly Number, and an Optimal SVM Boundcited by 52
- 2015Sample compression schemes for VC classescited by 46
- 2020Stable Sample Compression Schemes: New Applications and an Optimal SVM Margin Boundcited by 31
- 2018Unlabeled sample compression schemes and corner peelings for ample and maximum classescited by 16
- 2019Optimal Collusion-Free Teachingcited by 7
- 2017Quadratic Upper Bound for Recursive Teaching Dimension of Finite VC Classescited by 6
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