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

C
Check Point Blog
美团技术团队
Microsoft Security Blog
Microsoft Security Blog
大猫的无限游戏
大猫的无限游戏
T
Tailwind CSS Blog
G
Google Developers Blog
博客园 - 聂微东
爱范儿
爱范儿
博客园 - 叶小钗
J
Java Code Geeks
月光博客
月光博客
博客园 - Franky
MongoDB | Blog
MongoDB | Blog
The Cloudflare Blog
宝玉的分享
宝玉的分享
博客园 - 【当耐特】
Microsoft Azure Blog
Microsoft Azure Blog
The GitHub Blog
The GitHub Blog
小众软件
小众软件
WordPress大学
WordPress大学
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
Vercel News
Vercel News
Y
Y Combinator Blog
量子位

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
A Primal-Dual Continuous LP Method on the Multi-choice Mu...
T-H. Hubert Chan, Fei Chen · 2013-07-02 · via cs.DS updates on arXiv.org

The J-choice K-best secretary problem, also known as the (J,K)-secretary problem, is a generalization of the classical secretary problem. An algorithm for the (J,K)-secretary problem is allowed to make J choices and the payoff to be maximized is the expected number of items chosen among the K best items. Previous works analyzed the case when the total number n of items is finite, and considered what happens when n grows. However, for general J and K, the optimal solution for finite n is difficult to analyze. Instead, we prove a formal connection between the finite model and the infinite model, where there are countably infinite number of items, each attached with a random arrival time drawn independently and uniformly from [0,1]. We use primal-dual continuous linear programming techniques to analyze a class of infinite algorithms, which are general enough to capture the asymptotic behavior of the finite model with large number of items. Our techniques allow us to prove that the optimal solution can be achieved by the (J,K)-Threshold Algorithm, which has a nice "rational description" for the case K = 1.