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

月光博客
月光博客
N
Netflix TechBlog - Medium
罗磊的独立博客
博客园 - 聂微东
美团技术团队
GbyAI
GbyAI
Microsoft Security Blog
Microsoft Security Blog
Recent Commits to openclaw:main
Recent Commits to openclaw:main
博客园_首页
宝玉的分享
宝玉的分享
G
GRAHAM CLULEY
Microsoft Azure Blog
Microsoft Azure Blog
量子位
SecWiki News
SecWiki News
F
Fortinet All Blogs
J
Java Code Geeks
S
SegmentFault 最新的问题
V
V2EX
Martin Fowler
Martin Fowler
F
Full Disclosure
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
P
Proofpoint News Feed
S
Security Affairs
Application and Cybersecurity Blog
Application and Cybersecurity Blog
K
Kaspersky official blog
S
Secure Thoughts
S
Schneier on Security
MongoDB | Blog
MongoDB | Blog
博客园 - 三生石上(FineUI控件)
Cloudbric
Cloudbric
雷峰网
雷峰网
cs.CV updates on arXiv.org
cs.CV updates on arXiv.org
钛媒体:引领未来商业与生活新知
钛媒体:引领未来商业与生活新知
The Cloudflare Blog
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
爱范儿
爱范儿
V2EX - 技术
V2EX - 技术
H
Hackread – Cybersecurity News, Data Breaches, AI and More
腾讯CDC
阮一峰的网络日志
阮一峰的网络日志
Apple Machine Learning Research
Apple Machine Learning Research
H
Help Net Security
C
Check Point Blog
T
The Blog of Author Tim Ferriss
D
DataBreaches.Net
Hacker News - Newest:
Hacker News - Newest: "LLM"
G
Google Developers Blog
CTFtime.org: upcoming CTF events
CTFtime.org: upcoming CTF events
T
Tenable Blog
博客园 - 【当耐特】

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 Adaptive Multi-Round Allocation with Stochastic Arrivals The tractability landscape of diffusion alignment: regularization, rewards, and computational primitives Mistake-Bounded Language Generation Positional LSH: Binary Block Matrix Approximation for Attention with Linear Biases Learning-Augmented Scalable Linear Assignment Problem Optimization via Neural Dual Warm-Starts A Note on Non-Negative $L_1$-Approximating Polynomials Curvature Beyond Positivity: Greedy Guarantees for Arbitrary Submodular Functions Convex Optimization with Nested Evolving Feasible Sets On the Complexity of the Matching Problem of Regular Expressions with Backreferences Simple KNN-Based Outlier Detection Achieves Robust Clustering Online Allocation with Unknown Shared Supply Equivalence of Coarse and Fine-Grained Models for Learning with Distribution Shift Accelerated Relax-and-Round for Concave Coverage Problems Contrastive Identification and Generation in the Limit Quantizing With Randomized Hadamard Transforms: Efficient Heuristic Now Proven Nearly Optimal Attention Coresets On Computing Total Variation Distance Between Mixtures of Product Distributions Exact and Approximate Algorithms for Polytree Learning Provable Accuracy Collapse in Embedding-Based Representations under Dimensionality Mismatch New Bounds for Kernel Sums via Fast Spherical Embeddings Unlearning Offline Stochastic Multi-Armed Bandits Matroid Algorithms Under Size-Sensitive Independence Oracles On the Learning Curves of Revenue Maximization Asymptotically Robust Learning-Augmented Algorithms for Preemptive FIFO Buffer Management Flashback: A Reversible Bilateral Run-Peeling Decomposition of Strings Incremental Strongly Connected Components with Predictions Characterizing Admissible Objective Functions for Hierarchical Clustering Well-Conditioned Oblivious Perturbations in Linear Space Mathematical Foundations for Peer-to-Peer Lattice Computation Graph Neural Network-Informed Predictive Flows for Faster Ford-Fulkerson and PAC-Learnability A weighted angle distance on strings Towards Universal Convergence of Backward Error in Linear System Solvers Constant-Factor Approximations for Doubly Constrained Fair k-Center, k-Median and k-Means Tight Bounds for Learning Polyhedra with a Margin Efficiency of Proportional Mechanisms in Online Auto-Bidding Advertising Skyline-First Traversal as a Control Mechanism for Multi-Criteria Graph Search Constant-Factor Approximation for the Uniform Decision Tree Limited Perfect Monotonical Surrogates constructed using low-cost recursive linkage discovery with guaranteed output Query Lower Bounds for Diffusion Sampling Early Pruning for Public Transport Routing Adapting Dijkstra for Buffers and Unlimited Transfers Exploiting Low-Rank Structure in Max-K-Cut Problems Partial Optimality in the Preordering Problem High-accuracy log-concave sampling with stochastic queries Learning to Approximate Uniform Facility Location via Graph Neural Networks Linear Regression with Unknown Truncation Beyond Gaussian Features Adaptive Power Iteration Method for Differentially Private PCA Finite and Corruption-Robust Regret Bounds in Online Inverse Linear Optimization under M-Convex Action Sets Learning Mixture Models via Efficient High-dimensional Sparse Fourier Transforms Variance Computation for Weighted Model Counting with Knowledge Compilation Approach Deterministic Coreset for Lp Subspace Online Algorithms for Repeated Optimal Stopping: Balancing Baseline Guarantees and Regret Learned Static Function Data Structures Optimal hypersurface decision trees A Perfectly Truthful Calibration Measure The Geometry of LLM Quantization: GPTQ as Babai's Nearest Plane Algorithm Best Agent Identification for General Game Playing A Faster Generalized Two-Stage Approximate Top-K Fast and Simple Densest Subgraph with Predictions Smoothed Analysis of Learning from Positive Samples Ineffectiveness for Search and Undecidability of PCSP Meta-Problems Sample-Efficient Optimization over Generative Priors via Coarse Learnability Efficient distributional regression trees learning algorithms for calibrated non-parametric probabilistic forecasts Testing Noise Assumptions of Learning Algorithms Testing Support Size More Efficiently Than Learning Histograms Sharper Bounds for Chebyshev Moment Matching, with Applications Expander Hierarchies for Normalized Cuts on Graphs Multilayer Correlation Clustering Efficient Parameter Estimation of Truncated Boolean Product Distributions Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale: a self-calibrated randomized solution
Merge-width and First-Order Model Checking
Jan Dreier, Szymon Toruńczyk · 2025-02-25 · via cs.DS updates on arXiv.org

We introduce merge-width, a family of graph parameters that unifies several structural graph measures, including treewidth, degeneracy, twin-width, clique-width, and generalized coloring numbers. Our parameters are based on new decompositions called construction sequences. These are sequences of ever coarser partitions of the vertex set, where each pair of parts has a specified default connection, and all vertex pairs of the graph that differ from the default are marked as resolved. The radius-$r$ merge-width is the maximum number of parts reached from a vertex by following a path of at most $r$ resolved edges. Graph classes of bounded merge-width -- for which the radius-$r$ merge-width parameter can be bounded by a constant, for each fixed $r=1,2,3,\ldots$ -- include all classes of bounded expansion or of bounded twin-width, thus unifying two central notions from the Sparsity and Twin-width frameworks. Furthermore, they are preserved under first-order transductions, which attests to their robustness. We conjecture that classes of bounded merge-width are equivalent to the previously introduced classes of bounded flip-width. As our main result, we show that the model checking problem for first-order logic is fixed-parameter tractable on graph classes of bounded merge-width, assuming the input includes a witnessing construction sequence. This unites and extends two previous model checking results: the result of Dvořák, Král, and Thomas for classes of bounded expansion, and the result of Bonnet, Kim, Thomassé, and Watrigant for classes of bounded twin-width. Finally, we suggest future research directions that could impact the study of structural and algorithmic graph theory, in particular of monadically dependent graph classes, which we conjecture to coincide with classes of almost bounded merge-width.