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PAC Learning with Bandit Feedback: Sharp Sample Complexity in the Realizable Setting Algorithms with Polynomially-Improved Approximation Factors for the $2 \rightarrow q$ Norm, and Applications A computational phase transition for learning-to-sample from Ising models Covering vertices by sequential stars Fermi-Dirac machines as quantizations of neurons A Comprehensive Evaluation of Vertex Elimination Algorithms for Algorithmic Differentiation A Tight Bound on Localization of Electrical Flows Optimal Dimension-Free Sampling for Regularized Classification Reducing the Randomness in Partition Oracles for Bounded Degree Minor-Free Graphs Beyond the Half-Approximation: Fair and Efficient Online Class Matching Efficient Uniform Sampling of Surjections via their Profiles Tractable Maximization of Budgeted Phylogenetic Diversity on Networks Utilizing Node Scanwidth Fairness in Aggregation: Optimal Top-$k$ and Improved Full Ranking Learning-Augmented Online Scheduling with Parsimonious Preemption Entropy Equivalence Testing Lumberjack: Better Differentially Private Random Forests through Heavy Hitter Detection in Trees The Secretary Problem with a Stochastic Precursor Polynomial-Time Robust Multiclass Linear Classification under Gaussian Marginals Efficient Banzhaf-Based Data Valuation for $k$-Nearest Neighbors Classification Block-Sphere Vector Quantization An Approximation Algorithm for Graph Label Selection Iterative Chow Filtering for Learning with Distribution Shift Complexity of Non-Log-Concave Sampling in Fisher Information Stochastic Matching via Local Sparsification Finite Sample Bounds for Learning with Score Matching What is Learnable in Valiant's Theory of the Learnable? Provable Quantization with Randomized Hadamard Transform Min-Max Optimization Requires Exponentially Many Queries Fast and Compact Graph Cuts for the Boykov-Kolmogorov Algorithm A proximal gradient algorithm for composite log-concave sampling
A sufficient condition for characterizing the one-sided t...
Christine Awofeso, Patrick Greaves, Oded Lachish, Amit Levi, Fel · 2025-11-24 · via cs.DS updates on arXiv.org

We study property testing in the \emph{random neighbor oracle} model for graphs, originally introduced by Czumaj and Sohler [STOC 2019]. Specifically, we initiate the study of characterizing the graph families that are $H$-\emph{testable} in this model. A graph family $\mathcal{F}$ is $H$-testable if, for every graph $H$, $H$-\emph{freeness} (that is, not having a subgraph isomorphic to $H$) is testable with one-sided error on all inputs from $\mathcal{F}$. Czumaj and Sohler showed that for any $H$-testable family of graphs $\mathcal{F}$, the family of testable properties of $\mathcal{F}$ has a known characterization, a major goal in the study of property testing. Consequently, characterizing the collection of $H$-testable graph families will not only result in new characterizations, but will also exhaust this method of characterizing testable properties. We believe that our result is a substantial step towards this goal. Czumaj and Sohler further showed that the family of planar graphs is $H$-testable, as is any family of minor-free graphs. In this paper, we provide a sufficient and much broader criterion under which a family of graphs is $H$-testable. As a corollary, we obtain new characterizations for many families of graphs including: families that are closed under taking topological minors or immersions, geometric intersection graphs of low-density objects, euclidean nearest-neighbour graphs with bounded clique number, graphs with bounded crossing number (per edge), graphs with bounded queue- and stack number, and more. The criterion we provide is based on the \emph{$r$-admissibility} graph measure from the theory of sparse graph families initiated by Nesetril and Ossona de Mendez. Proving that specific families of graphs satisfy this criterion is an active area of research, consequently, the implications of this paper may be strengthened in the future.