Expressivity of Contradiction Graphs

Abstract:We study the contradiction graphs associated with a binary concept class. For a class $H\subseteq\{0,1\}^X$, the order-$m$ contradiction graph $G_m(H)$ has as vertices the $H$-realizable labeled sequences of length $m$, with two vertices adjacent when the two sequences assign opposite labels to some common domain point. First, we identify a graph-theoretic property that determines the threshold predicate $\operatorname{VCdim}(H)\ge m$. Consequently, the sequence $(G_m(H))_{m\ge1}$ determines the exact VC dimension and, in particular, distinguishes finite from infinite VC dimension, answering a question posed by Alon et al. (2024). We then generalize this result by proving that the sequence of contradiction graphs determines, up to signed relabeling, the realizable datasets of any fixed length. Thus, any learning-theoretic property determined by the realizable datasets of a fixed length and invariant under signed relabeling can be recovered from the contradiction graph sequence. As an application, we explicitly provide a characterization of Littlestone dimension.

Machine Learning (stat.ML); Discrete Mathematics (cs.DM); Machine Learning (cs.LG)

arXiv:2605.20434 [stat.ML]

(or arXiv:2605.20434v2 [stat.ML] for this version)

https://doi.org/10.48550/arXiv.2605.20434 Focus to learn more arXiv-issued DOI via DataCite

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