Variance reduction in frequency estimators via control variates method
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:183-193, 2021.
Generating succinct summaries (also known as sketches) of massive data streams is becoming increasingly important. Such a task typically requires fast, accurate, and small space algorithms in order to support the downstream applications, mainly in areas such as data analysis, machine learning and data mining. A fundamental and well-studied problem in this context is that of estimating the frequencies of the items appearing in a data stream. The Count-Min-Sketch (Cormode and Muthukrishnan, J. Algorithms, 55(1):58–75, 2005) and Count-Sketch (Charikar et al., Theor. Comput. Sci., 312(1):3–15, 2004) are two known classical algorithms for this purpose. However, a limitation of these techniques is that the variance of their estimate tends to be large. In this work, we address this problem and suggest a technique that reduces the variance in their respective estimates, at the cost of little computational overhead. Our technique relies on the classical Control-Variate trick (Lavenberg and Welch, Manage. Sci., 27:322–335, 1981) used for reducing variance in Monte-Carlo simulation. We present a theoretical analysis of our proposal by carefully choosing the control variates and complement them with experiments on synthetic as well as real-world datasets.