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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
Near-Optimal Vertex Fault-Tolerant Labels for Steiner Con...
Koustav Bhanja, Asaf Petruschka · 2025-06-29 · via cs.DS updates on arXiv.org

We present a compact labeling scheme for determining whether a designated set of terminals in a graph remains connected after any $f$ (or less) vertex failures occur. An $f$-FT Steiner connectivity labeling scheme for an $n$-vertex graph $G=(V,E)$ with terminal set $U \subseteq V$ provides labels to the vertices of $G$, such that given only the labels of any subset $F \subseteq V$ with $|F| \leq f$, one can determine if $U$ remains connected in $G-F$. The main complexity measure is the maximum label length. The special case $U=V$ of global connectivity has been recently studied by Jiang, Parter, and Petruschka, who provided labels of $n^{1-1/f} \cdot \mathrm{poly}(f,\log n)$ bits. This is near-optimal (up to $\mathrm{poly}(f,\log n)$ factors) by a lower bound of Long, Pettie and Saranurak. Our scheme achieves labels of $|U|^{1-1/f} \cdot \mathrm{poly}(f, \log n)$ for general $U \subseteq V$, which is near-optimal for any given size $|U|$ of the terminal set. To handle terminal sets, our approach differs from Jiang et al. We use a well-structured Steiner tree for $U$ produced by a decomposition theorem of Duan and Pettie, and bypass the need for Nagamochi-Ibaraki sparsification.