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

博客园_首页
B
Blog
V
V2EX
T
Tailwind CSS Blog
Hugging Face - Blog
Hugging Face - Blog
博客园 - 【当耐特】
博客园 - 聂微东
博客园 - 叶小钗
博客园 - 三生石上(FineUI控件)
The Cloudflare Blog
J
Java Code Geeks
H
Help Net Security
雷峰网
雷峰网
Apple Machine Learning Research
Apple Machine Learning Research
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Engineering at Meta
Engineering at Meta
F
Fortinet All Blogs
Martin Fowler
Martin Fowler
D
Docker
L
LangChain Blog
人人都是产品经理
人人都是产品经理
爱范儿
爱范儿
WordPress大学
WordPress大学
V
Visual Studio 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
Hidden Permutations to the Rescue: Multi-Pass Semi-Stream...
Sepehr Assadi, Janani Sundaresan · 2023-10-09 · via cs.DS updates on arXiv.org

We prove that any semi-streaming algorithm for $(1-ε)$-approximation of maximum bipartite matching requires \[ Ω(\frac{\log{(1/ε)}}{\log{(1/β)}}) \] passes, where $β\in (0,1)$ is the largest parameter so that an $n$-vertex graph with $n^β$ edge-disjoint induced matchings of size $Θ(n)$ exist (such graphs are referred to as RS graphs). Currently, it is known that \[ Ω(\frac{1}{\log\log{n}}) \leqslant β\leqslant 1-Θ(\frac{\log^*{n}}{\log{n}}) \] and closing this huge gap between upper and lower bounds has remained a notoriously difficult problem in combinatorics. Under the plausible hypothesis that $β= Ω(1)$, our lower bound result provides the first pass-approximation lower bound for (small) constant approximation of matchings in the semi-streaming model, a longstanding open question in the graph streaming literature. Our techniques are based on analyzing communication protocols for compressing (hidden) permutations. Prior work in this context relied on reducing such problems to Boolean domain and analyzing them via tools like XOR Lemmas and Fourier analysis on Boolean hypercube. In contrast, our main technical contribution is a hardness amplification result for permutations through concatenation in place of prior XOR Lemmas. This result is proven by analyzing permutations directly via simple tools from group representation theory combined with detailed information-theoretic arguments, and can be of independent interest.