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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
Sorting wild pigs
2023-04-24 · via cs.DS updates on arXiv.org

Chjara, breeder in Carg{è}se, has n wild pigs. She would like to sort her herd by weight to better meet the demands of her buyers. Each beast has a distinct weight, alas unknown to Chjara. All she has at her disposal is a Roberval scale, which allows her to compare two pigs only at the cost of an acrobatic manoeuvre. The balance, quite old, can break at any time. Chjara therefore wants to sort his herd in a minimum of weighings, but also to have a good estimate of the result after each weighing.To help Chjara, we pose the problem of finding a good anytime sorting algorithm, in the sense of Kendall's tau distance between provisional result and perfectly sorted list, and we bring the following contributions:- We introduce Corsort, a family of anytime sorting algorithms based on estimators.- By simulation, we show that a well-configured Corsort has a near-optimal termination time, and provides better intermediate estimates than the best sorting algorithms we are aware of.