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cs.DS updates on arXiv.org

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
New Graph and Hypergraph Container Lemmas with Applicatio...
Eric Blais, Cameron Seth · 2024-03-28 · via cs.DS updates on arXiv.org

The graph and hypergraph container methods are powerful tools with a wide range of applications across combinatorics. Recently, Blais and Seth (FOCS 2023) showed that the graph container method is particularly well-suited for the analysis of the natural canonical tester for two fundamental graph properties: having a large independent set and $k$-colorability. In this work, we show that the connection between the container method and property testing extends further along two different directions. First, we show that the container method can be used to analyze the canonical tester for many other properties of graphs and hypergraphs. We introduce a new hypergraph container lemma and use it to give an upper bound of $\widetilde{O}(kq^3/ε)$ on the sample complexity of $ε$-testing satisfiability, where $q$ is the number of variables per constraint and $k$ is the size of the alphabet. This is the first upper bound for the problem that is polynomial in all of $k$, $q$ and $1/ε$. As a corollary, we get new upper bounds on the sample complexity of the canonical testers for hypergraph colorability and for every semi-homogeneous graph partition property. Second, we show that the container method can also be used to study the query complexity of (non-canonical) graph property testers. This result is obtained by introducing a new container lemma for the class of all independent set stars, a strict superset of the class of all independent sets. We use this container lemma to give a new upper bound of $\widetilde{O}(ρ^5/ε^{7/2})$ on the query complexity of $ε$-testing the $ρ$-independent set property. This establishes for the first time the non-optimality of the canonical tester for a non-homogeneous graph partition property.