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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
Dense Peelable Random Uniform Hypergraphs
Martin Dietzfelbinger, Stefan Walzer · 2019-07-10 · via cs.DS updates on arXiv.org

We describe a new family of $k$-uniform hypergraphs with independent random edges. The hypergraphs have a high probability of being peelable, i.e. to admit no sub-hypergraph of minimum degree $2$, even when the edge density (number of edges over vertices) is close to $1$. In our construction, the vertex set is partitioned into linearly arranged segments and each edge is incident to random vertices of $k$ consecutive segments. Quite surprisingly, the linear geometry allows our graphs to be peeled "from the outside in". The density thresholds $f_k$ for peelability of our hypergraphs ($f_3 \approx 0.918$, $f_4 \approx 0.977$, $f_5 \approx 0.992$, ...) are well beyond the corresponding thresholds ($c_3 \approx 0.818$, $c_4 \approx 0.772$, $c_5 \approx 0.702$, ...) of standard $k$-uniform random hypergraphs. To get a grip on $f_k$, we analyse an idealised peeling process on the random weak limit of our hypergraph family. The process can be described in terms of an operator on functions and $f_k$ can be linked to thresholds relating to the operator. These thresholds are then tractable with numerical methods. Random hypergraphs underlie the construction of various data structures based on hashing. These data structures frequently rely on peelability of the hypergraph or peelability allows for simple linear time algorithms. To demonstrate the usefulness of our construction, we used our $3$-uniform hypergraphs as a drop-in replacement for the standard $3$-uniform hypergraphs in a retrieval data structure by Botelho et al. This reduces memory usage from $1.23m$ bits to $1.12m$ bits ($m$ being the input size) with almost no change in running time.