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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
Tolerant Bipartiteness Testing in Dense Graphs
Arijit Ghosh, Gopinath Mishra, Rahul Raychaudhury, Sayantan Sen · 2022-04-26 · via cs.DS updates on arXiv.org

Bipartite testing has been a central problem in the area of property testing since its inception in the seminal work of Goldreich, Goldwasser and Ron [FOCS'96 and JACM'98]. Though the non-tolerant version of bipartite testing has been extensively studied in the literature, the tolerant variant is not well understood. In this paper, we consider the following version of tolerant bipartite testing: Given a parameter $\varepsilon \in (0,1)$ and access to the adjacency matrix of a graph $G$, we can decide whether $G$ is $\varepsilon$-close to being bipartite or $G$ is at least $(2+Ω(1))\varepsilon$-far from being bipartite, by performing $\widetilde{\mathcal{O}}\left(\frac{1}{\varepsilon ^3}\right)$ queries and in $2^{\widetilde{\mathcal{O}}(1/\varepsilon)}$ time. This improves upon the state-of-the-art query and time complexities of this problem of $\widetilde{\mathcal{O}}\left(\frac{1}{\varepsilon ^6}\right)$ and $2^{\widetilde{\mathcal{O}}(1/\varepsilon^2)}$, respectively, from the work of Alon, Fernandez de la Vega, Kannan and Karpinski (STOC'02 and JCSS'03), where $\widetilde{\mathcal{O}}(\cdot)$ hides a factor polynomial in $\log \frac{1}{\varepsilon}$.