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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
Connectivity Labeling Schemes for Edge and Vertex Faults ...
Yaowei Long, Seth Pettie, Thatchaphol Saranurak · 2024-10-25 · via cs.DS updates on arXiv.org

We consider the problem of assigning short labels to the vertices and edges of a graph $G$ so that given any query $\langle s,t,F\rangle$ with $|F|\leq f$, we can determine whether $s$ and $t$ are still connected in $G-F$, given only the labels of $F\cup\{s,t\}$. This problem has been considered when $F\subset E$ (edge faults), where correctness is guaranteed with high probability (w.h.p.) or deterministically, and when $F\subset V$ (vertex faults), both w.h.p.~and deterministically. Our main results are as follows. [Deterministic Edge Faults.] We give a new deterministic labeling scheme for edge faults that uses $\tilde{O}(\sqrt{f})$-bit labels, which can be constructed in polynomial time. This improves on Dory and Parter's [PODC 2021] existential bound of $O(f\log n)$ (requiring exponential time to compute) and the efficient $\tilde{O}(f^2)$-bit scheme of Izumi, Emek, Wadayama, and Masuzawa [PODC 2023]. Our construction uses an improved edge-expander hierarchy and a distributed coding technique based on Reed-Solomon codes. [Deterministic Vertex Faults.] We improve Parter, Petruschka, and Pettie's [STOC 2024] deterministic $O(f^7\log^{13} n)$-bit labeling scheme for vertex faults to $O(f^4\log^{7.5} n)$ bits, using an improved vertex-expander hierarchy and better sparsification of shortcut graphs. [Randomized Edge/Verex Faults.] We improve the size of Dory and Parter's [PODC 2021] randomized edge fault labeling scheme from $O(\min\{f+\log n, \log^3 n\})$ bits to $O(\min\{f+\log n, \log^2 n\log f\})$ bits, shaving a $\log n/\log f$ factor. We also improve the size of Parter, Petruschka, and Pettie's [STOC 2024] randomized vertex fault labeling scheme from $O(f^3\log^5 n)$ bits to $O(f^2\log^6 n)$ bits, which comes closer to their $Ω(f)$-bit lower bound.