基于时间衰减模型的学习增强矩估计
Learning-Augmented Moment Estimation on Time-Decay Models
AI总结：
本文提出基于时间衰减模型的学习增强算法，用于改进矩估计、频率估计等基本问题的空间效率，并通过实验验证其实际性能。
摘要：
受机器学习广泛应用和成功的启发，近期有一系列研究探讨了在流模型中学习增强算法。这些结果表明，对于使用机器学习模型实现的自然和实用的 oracle，我们可以获得在空间效率上得到改进的流算法，这些算法在理论上是无法实现的。另一方面，当我们处理权重不均的项目时，我们的理解就更加有限，例如在滑动窗口模型中，较旧的数据必须从数据集中删除，例如通过隐私法规。在本文中，我们利用数据集的 heavy-hitters oracle，为一系列基本问题（如范数/矩估计、频率估计、级联范数和矩估计）在时间衰减设置中提供学习增强算法。我们通过一系列实验证明了我们的算法在真实和合成数据集上的实际效率。
英文摘要：
Motivated by the prevalence and success of machine learning, a line of recent work has studied learning-augmented algorithms in the streaming model. These results have shown that for natural and practical oracles implemented with machine learning models, we can obtain streaming algorithms with improved space efficiency that are otherwise provably impossible. On the other hand, our understanding is much more limited when items are weighted unequally, for example, in the sliding-window model, where older data must be expunged from the dataset, e.g., by privacy regulation laws. In this paper, we utilize an oracle for the heavy-hitters of datasets to give learning-augmented algorithms for a number of fundamental problems, such as norm/moment estimation, frequency estimation, cascaded norms, and rectangular moment estimation, in the time-decay setting. We complement our theoretical results with a number of empirical evaluations that demonstrate the practical efficiency of our algorithms on real and synthetic datasets.
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。
