惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

Jina AI
Jina AI
Apple Machine Learning Research
Apple Machine Learning Research
宝玉的分享
宝玉的分享
M
MIT News - Artificial intelligence
S
SegmentFault 最新的问题
博客园 - 叶小钗
量子位
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
酷 壳 – CoolShell
酷 壳 – CoolShell
博客园 - Franky
博客园 - 司徒正美
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
人人都是产品经理
人人都是产品经理
Hugging Face - Blog
Hugging Face - Blog
V
Visual Studio Blog
阮一峰的网络日志
阮一峰的网络日志
博客园 - 【当耐特】
Google DeepMind News
Google DeepMind News
L
LangChain Blog
Stack Overflow Blog
Stack Overflow Blog
博客园_首页
U
Unit 42
月光博客
月光博客
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC

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
A Unified Framework of Light Spanners II: Fine-Grained Op...
Hung Le, Shay Solomon · 2021-11-27 · via cs.DS updates on arXiv.org

Seminal works on light spanners over the years provide spanners with optimal lightness in various graph classes, such as in general graphs, Euclidean spanners, and minor-free graphs. Three shortcomings of previous works on light spanners are: (1) The techniques are ad hoc per graph class, and thus can't be applied broadly. (2) The runtimes of these constructions are almost always sub-optimal, and usually far from optimal. (3) These constructions are optimal in the standard and crude sense, but not in a refined sense that takes into account a wider range of involved parameters. This work aims at addressing these shortcomings by presenting a unified framework of light spanners in a variety of graph classes. Informally, the framework boils down to a transformation from sparse spanners to light spanners; since the state-of-the-art for sparse spanners is much more advanced than that for light spanners, such a transformation is powerful. Our framework is developed in two papers. The current paper is the second of the two -- it builds on the basis of the unified framework laid in the first paper, and then strengthens it to achieve more refined optimality bounds for several graph classes. Among various applications and implications of our framework, we highlight here the following: For $K_r$-minor-free graphs, we provide a $(1+ε)$-spanner with lightness $\tilde{O}_{r,ε}( \frac{r}ε + \frac{1}{ε^2})$, improving the lightness bound $\tilde{O}_{r,ε}( \frac{r}{ε^3})$ of Borradaile, Le and Wulff-Nilsen. We complement our upper bound with a lower bound construction, for which any $(1+ε)$-spanner must have lightness $Ω(\frac{r}ε + \frac{1}{ε^2})$. We note that the quadratic dependency on $1/ε$ we proved here is surprising, as the prior work suggested that the dependency on $ε$ should be $1/ε$.