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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
An Automatic Speedup Theorem for Distributed Problems
Sebastian Brandt · 2019-02-26 · via cs.DS updates on arXiv.org

Recently, Brandt et al. [STOC'16] proved a lower bound for the distributed Lovász Local Lemma, which has been conjectured to be tight for sufficiently relaxed LLL criteria by Chang and Pettie [FOCS'17]. At the heart of their result lies a speedup technique that, for graphs of girth at least $2t+2$, transforms any $t$-round algorithm for one specific LLL problem into a $(t-1)$-round algorithm for the same problem. We substantially improve on this technique by showing that such a speedup exists for any locally checkable problem $Π$, with the difference that the problem $Π_1$ the inferred $(t-1)$-round algorithm solves is not (necessarily) the same problem as $Π$. Our speedup is automatic in the sense that there is a fixed procedure that transforms a description for $Π$ into a description for $Π_1$ and reversible in the sense that any $(t-1)$-round algorithm for $Π_1$ can be transformed into a $t$-round algorithm for $Π$. In particular, for any locally checkable problem $Π$ with exact deterministic time complexity $T(n, Δ) \leq t$ on graphs with $n$ nodes, maximum node degree $Δ$, and girth at least $2t+2$, there is a sequence of problems $Π_1, Π_2, \dots$ with time complexities $T(n, Δ)-1, T(n, Δ)-2, \dots$, that can be inferred from $Π$. As a first application of our generalized speedup, we solve a long-standing open problem of Naor and Stockmeyer [STOC'93]: we show that weak $2$-coloring in odd-degree graphs cannot be solved in $o(\log^* Δ)$ rounds, thereby providing a matching lower bound to their upper bound.