arXiv 2202.042622022-02-09cs.DScs.LG

Parsimonious Learning-Augmented Caching

University of California, Merced（加州大学默塞德分校）；Google Research（谷歌研究院）

Sungjin Im, Ravi Kumar, Aditya Petety, Manish Purohit

英文摘要：

Learning-augmented algorithms -- in which, traditional algorithms are augmented with machine-learned predictions -- have emerged as a framework to go beyond worst-case analysis. The overarching goal is to design algorithms that perform near-optimally when the predictions are accurate yet retain certain worst-case guarantees irrespective of the accuracy of the predictions. This framework has been successfully applied to online problems such as caching where the predictions can be used to alleviate uncertainties. In this paper we introduce and study the setting in which the learning-augmented algorithm can utilize the predictions parsimoniously. We consider the caching problem -- which has been extensively studied in the learning-augmented setting -- and show that one can achieve quantitatively similar results but only using a sublinear number of predictions.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

使用减少数量预测的缓存和MTS算法

Algorithms for Caching and MTS with reduced number of predictions

University of Amsterdam（阿姆斯特丹大学）；Bocconi University（博科尼大学）

本文为缓存和一般MTS设计节俭使用动作预测的算法，在预测数量受限时刻画一致性与平滑性，并在无限制时恢复已有最优保证。

2024-04-10cs.DScs.LG

简约学习增强的在线度量匹配

Parsimonious Learning-Augmented Online Metric Matching

Institute of Computer Science, University of Wrocław, Wrocław, Poland（沃斯克拉大学计算机科学研究所）

针对在线度量匹配问题，提出一种简约学习增强算法，通过虚拟预测填补缺失预测，并建立性能下界，实验验证了其有效性。

2026-05-26cs.DScs.LG

在不牺牲1-一致性的情况下高效增强学习增强型缓存的鲁棒性

Robustifying Learning-Augmented Caching Efficiently without Compromising 1-Consistency

Zhejiang University（浙江大学）；Nanyang Technological University（南洋理工大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）

针对在线缓存问题中学习增强型算法缺乏鲁棒性或牺牲1-一致性的问题，提出轻量级Guard框架，在保留1-一致性的同时将鲁棒性增强至2H_{k-1}+2，且仅增加O(1)开销，实现当前最佳权衡并经实验验证有效。

2025-11-14cs.DScs.LG

具有节俭抢占的学习增强在线调度

Learning-Augmented Online Scheduling with Parsimonious Preemption

University of California, Santa Cruz（加州大学圣克鲁兹分校）；Institut für Mathematik, Technische Universität Berlin（柏林技术大学数学研究所）

针对在线调度中算法性能与抢占开销的权衡，提出在准确预测下每作业仅需O(1)次抢占且竞争比为O(1)的学习增强算法，并将理论扩展到无关机和可塑机。

2026-05-22cs.DScs.LG

稀疏学习增强近似算法用于稠密 $\mathcal{NP}$-难问题实例

Parsimonious Learning-Augmented Approximations for Dense Instances of $\mathcal{NP}$-hard Problems

本文提出学习增强框架，用对数个一比特预测加速稠密NP难问题的近似算法，保证一致性、平滑性和鲁棒性。

2024-05-22cs.DS

Better and Simpler Learning-Augmented Online Caching

Lykouris and Vassilvitskii (ICML 2018) introduce a model of online caching with machine-learned advice, where each page request additionally comes with a prediction of when that page will next be requested. In this model, a natural goal is to design algorithms that (1) perform well when the advice is accurate and (2) remain robust in the worst case a la traditional competitive analysis. Lykouris and Vassilvitskii give such an algorithm by adapting the Marker algorithm to the learning-augmented setting. In a recent work, Rohatgi (SODA 2020) improves on their result with an approach also inspire…

2020-05-28cs.DS

更多相关论文收起更多论文

Near-Optimal Bounds for Online Caching with Machine Learned Advice

In the model of online caching with machine learned advice, introduced by Lykouris and Vassilvitskii, the goal is to solve the caching problem with an online algorithm that has access to next-arrival predictions: when each input element arrives, the algorithm is given a prediction of the next time when the element will reappear. The traditional model for online caching suffers from an $Ω(\log k)$ competitive ratio lower bound (on a cache of size $k$). In contrast, the augmented model admits algorithms which beat this lower bound when the predictions have low error, and asymptotically match the…

2019-10-29cs.DS

学习增强型算法：保证、构造机制与系统级含义

Learning-Augmented Algorithms: Guarantees, Construction Mechanisms, and System-Level Implications

School of Software Technology, Zhejiang University（浙江大学软件学院）；College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；School of Computer Science and Engineering, Nanyang Technological University（南洋理工大学计算机科学与工程学院）

本综述针对学习增强型算法，综合了多领域的构造机制与相关权衡，给出了端到端推理的充分条件，同时划定了相关开放问题。

2026-09-09cs.DScs.LG

NP-hard排列问题的多项式时间学习增强算法

Polynomial Time Learning-Augmented Algorithms for NP-hard Permutation Problems

本文提出一种用于NP-hard排列问题的学习增强框架，在给定元素对相对顺序的预测且预测准确率至少为$1/2+ε$时，能以高概率在多项式时间内解决调度、网络设计等优化问题。

2025-02-02cs.DS

Online Algorithms with Multiple Predictions

Duke University（杜克大学）；IIT Delhi（德里印度理工学院）

This paper studies online algorithms augmented with multiple machine-learned predictions. While online algorithms augmented with a single prediction have been extensively studied in recent years, the literature for the multiple predictions setting is sparse. In this paper, we give a generic algorithmic framework for online covering problems with multiple predictions that obtains an online solution that is competitive against the performance of the best predictor. Our algorithm incorporates the use of predictions in the classic potential-based analysis of online algorithms. We apply our algorit…

2022-07-13cs.DScs.LG

带有显式预测器的学习增强算法

Learning-Augmented Algorithms with Explicit Predictors

Bocconi University（博科尼大学）；Tel Aviv University（特拉维夫大学）；Google Research（谷歌研究院）；Technion（以色列理工学院）

针对在线算法中黑盒预测器范式的局限，本研究提出带显式预测器的学习增强算法，让预测器随输入增量学习并适配任务，在缓存、调度等基础问题上取得了优于先前工作的性能界。

2024-03-12cs.DScs.LG

具有不可靠指导的在线算法

Online Algorithms with Unreliable Guidance

TU Berlin, Germany（柏林技术大学，德国）；Technion, Israel（技术学院，以色列）；Reykjavik University, Iceland（雷克雅未克大学，冰岛）；TU Berlin and Weizenbaum Institute, Germany（柏林技术大学和魏泽恩鲍姆研究所，德国）

本文提出了一种名为OAG的在线算法模型，通过请求-回答游戏的视角，分离预测与算法组件，构建了通用分析框架，从而开发出首个通用编译器DTB，将标准在线算法转化为学习增强型算法，并在三个经典问题中取得新的性能平衡和最优解。

2026-05-16cs.AIcs.DS

多专家在线覆盖问题

Online Covering with Multiple Experts

LIG, University Grenoble-Alpes（格勒诺布尔-阿尔卑斯大学LIG实验室）

本文提出多专家在线覆盖算法，引入动态基准，实现O(log K)竞争比，并提供了在线组合多个算法的全新视角。

2023-12-22cs.DMcs.DScs.LG