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

腾讯CDC
The Cloudflare Blog
IT之家
IT之家
V
V2EX
雷峰网
雷峰网
MyScale Blog
MyScale Blog
P
Proofpoint News Feed
Stack Overflow Blog
Stack Overflow Blog
博客园 - Franky
Engineering at Meta
Engineering at Meta
S
SegmentFault 最新的问题
GbyAI
GbyAI
Microsoft Azure Blog
Microsoft Azure Blog
博客园 - 司徒正美
云风的 BLOG
云风的 BLOG
小众软件
小众软件
博客园 - 叶小钗
Blog — PlanetScale
Blog — PlanetScale
C
Check Point Blog
A
About on SuperTechFans
B
Blog
月光博客
月光博客
宝玉的分享
宝玉的分享
Last Week in AI
Last Week in AI

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
Near Isometric Terminal Embeddings for Doubling Metrics
Michael Elkin, Ofer Neiman · 2018-02-22 · via cs.DS updates on arXiv.org

Given a metric space $(X,d)$, a set of terminals $K\subseteq X$, and a parameter $t\ge 1$, we consider metric structures (e.g., spanners, distance oracles, embedding into normed spaces) that preserve distances for all pairs in $K\times X$ up to a factor of $t$, and have small size (e.g. number of edges for spanners, dimension for embeddings). While such terminal (aka source-wise) metric structures are known to exist in several settings, no terminal spanner or embedding with distortion close to 1, i.e., $t=1+ε$ for some small $0<ε<1$, is currently known. Here we devise such terminal metric structures for {\em doubling} metrics, and show that essentially any metric structure with distortion $1+ε$ and size $s(|X|)$ has its terminal counterpart, with distortion $1+O(ε)$ and size $s(|K|)+1$. In particular, for any doubling metric on $n$ points, a set of $k=o(n)$ terminals, and constant $0<ε<1$, there exists: (1) A spanner with stretch $1+ε$ for pairs in $K\times X$, with $n+o(n)$ edges. (2) A labeling scheme with stretch $1+ε$ for pairs in $K\times X$, with label size $\approx \log k$. (3) An embedding into $\ell_\infty^d$ with distortion $1+ε$ for pairs in $K\times X$, where $d=O(\log k)$. Moreover, surprisingly, the last two results apply if only $K$ is a doubling metric, while $X$ can be arbitrary.