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

F
Fortinet All Blogs
Recent Announcements
Recent Announcements
H
Help Net Security
Y
Y Combinator Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
有赞技术团队
有赞技术团队
小众软件
小众软件
Last Week in AI
Last Week in AI
U
Unit 42
Google DeepMind News
Google DeepMind News
博客园 - 司徒正美
H
Hackread – Cybersecurity News, Data Breaches, AI and More
J
Java Code Geeks
Microsoft Security Blog
Microsoft Security Blog
G
Google Developers Blog
N
Netflix TechBlog - Medium
Blog — PlanetScale
Blog — PlanetScale
云风的 BLOG
云风的 BLOG
V
V2EX
博客园 - 聂微东
人人都是产品经理
人人都是产品经理
博客园 - 三生石上(FineUI控件)
阮一峰的网络日志
阮一峰的网络日志
爱范儿
爱范儿

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
Palette Sparsification for Graphs with Sparse Neighborhoods
Abhishek Dhawan · 2024-08-16 · via cs.DS updates on arXiv.org

A seminal palette sparsification result of Assadi, Chen, and Khanna states that in every $n$-vertex graph of maximum degree $Δ$, sampling $Θ(\log n)$ colors per vertex from $\{1, \ldots, Δ+1\}$ almost certainly allows for a proper coloring from the sampled colors. Alon and Assadi extended this work proving a similar result for $O\left(Δ/\log Δ\right)$-coloring triangle-free graphs. Apart from being interesting results from a combinatorial standpoint, their results have various applications to the design of graph coloring algorithms in different models of computation. In this work, we focus on locally sparse graphs, i.e., graphs with sparse neighborhoods. We say a graph $G = (V, E)$ is $k$-locally-sparse if for each vertex $v \in V$, the subgraph $G[N(v)]$ contains at most $k$ edges. A celebrated result of Alon, Krivelevich, and Sudakov shows that such graphs are $O(Δ/\log (Δ/\sqrt{k}))$-colorable. For any $α\in (0, 1)$ and $k \ll Δ^{2α}$, let $G$ be a $k$-locally-sparse graph. For $q = Θ\left(Δ/\log \left(Δ^α/\sqrt{k}\right)\right)$, we show that sampling $O\left(Δ^α+ \sqrt{\log n}\right)$ colors per vertex is sufficient to obtain a proper $q$-coloring of $G$ from the sampled colors. Setting $k = 1$ recovers the aforementioned result of Alon and Assadi for triangle-free graphs. A key element in our proof is a proposition regarding correspondence coloring in the so-called color-degree setting, which improves upon recent work of Anderson, Kuchukova, and the author and is of independent interest.