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

人人都是产品经理
人人都是产品经理
量子位
月光博客
月光博客
罗磊的独立博客
宝玉的分享
宝玉的分享
博客园_首页
酷 壳 – CoolShell
酷 壳 – CoolShell
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
WordPress大学
WordPress大学
博客园 - 叶小钗
博客园 - 聂微东
阮一峰的网络日志
阮一峰的网络日志
V
V2EX
雷峰网
雷峰网
博客园 - 三生石上(FineUI控件)
Jina AI
Jina AI
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - Franky
美团技术团队
爱范儿
爱范儿
V
Visual Studio Blog
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
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Y
Y Combinator Blog

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
Insertion Time of Random Walk Cuckoo Hashing below the Pe...
Stefan Walzer · 2022-02-11 · via cs.DS updates on arXiv.org

Most hash tables have an insertion time of $O(1)$, possibly qualified as expected and/or amortised. While insertions into cuckoo hash tables indeed seem to take $O(1)$ expected time in practice, only polylogarithmic guarantees are proven in all but the simplest of practically relevant cases. Given the widespread use of cuckoo hashing to implement compact dictionaries and Bloom filter alternatives, closing this gap is an important open problem for theoreticians. In this paper, we show that random walk insertions into cuckoo hash tables take $O(1)$ expected amortised time when any number $k \geq 3$ of hash functions is used and the load factor is below the corresponding peeling threshold (e.g. $\approx 0.81$ for $k = 3$). To our knowledge, this is the first meaningful guarantee for constant time insertion for cuckoo hashing that works for $k \in \{3,\dots,9\}$. In addition to being useful in its own right, we hope that our key-centred analysis method can be a stepping stone on the path to the true end goal: $O(1)$ time insertions for all load factors below the load threshold (e.g. $\approx 0.91$ for $k = 3$).