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Unlabeled Sample Compression Schemes and Corner Peelings for Ample and Maximum Classes

Authors Jérémie Chalopin , Victor Chepoi , Shay Moran , Manfred K. Warmuth



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Author Details

Jérémie Chalopin
  • CNRS, Aix-Marseille Université, Université de Toulon, LIS, Marseille, France
Victor Chepoi
  • Aix-Marseille Université, CNRS, Université de Toulon, LIS, Marseille, France
Shay Moran
  • Department of Computer Science, Princeton University, Princeton, USA
Manfred K. Warmuth
  • Computer Science Department, University of California, Santa Cruz, USA

Acknowledgements

The authors are grateful to Olivier Bousquet for insightful discussions and to the anonymous referees for useful remarks that helped improving the presentation of this work.

Cite AsGet BibTex

Jérémie Chalopin, Victor Chepoi, Shay Moran, and Manfred K. Warmuth. Unlabeled Sample Compression Schemes and Corner Peelings for Ample and Maximum Classes. In 46th International Colloquium on Automata, Languages, and Programming (ICALP 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 132, pp. 34:1-34:15, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/LIPIcs.ICALP.2019.34

Abstract

We examine connections between combinatorial notions that arise in machine learning and topological notions in cubical/simplicial geometry. These connections enable to export results from geometry to machine learning. Our first main result is based on a geometric construction by H. Tracy Hall (2004) of a partial shelling of the cross-polytope which can not be extended. We use it to derive a maximum class of VC dimension 3 that has no corners. This refutes several previous works in machine learning from the past 11 years. In particular, it implies that the previous constructions of optimal unlabeled compression schemes for maximum classes are erroneous. On the positive side we present a new construction of an optimal unlabeled compression scheme for maximum classes. We leave as open whether our unlabeled compression scheme extends to ample (a.k.a. lopsided or extremal) classes, which represent a natural and far-reaching generalization of maximum classes. Towards resolving this question, we provide a geometric characterization in terms of unique sink orientations of the 1-skeletons of associated cubical complexes.

Subject Classification

ACM Subject Classification
  • Mathematics of computing → Combinatorics
  • Theory of computation → Machine learning theory
  • Theory of computation → Computational geometry
Keywords
  • VC-dimension
  • sample compression
  • Sauer-Shelah-Perles lemma
  • Sandwich lemma
  • maximum class
  • ample/extremal class
  • corner peeling
  • unique sink orientation

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