Improved Frequency Estimation Algorithms with and without Predictions

Anders Aamand, Justin Y. Chen, Huy Nguyen, Sandeep Silwal, Ali Vakilian

Keywords: learning-augmented algorithms, algorithms with predictions, data-driven algorithms, sublinear, streaming, frequency estimation, sketching

TL;DR: We give improved learning-augmented algorithms for frequency estimation.

Abstract: Estimating frequencies of elements appearing in a data stream is a key task in large-scale data analysis. Popular sketching approaches to this problem (e.g., CountMin and CountSketch) come with worst-case guarantees that probabilistically bound the error of the estimated frequencies for any possible input. The work of Hsu et al.~(2019) introduced the idea of using machine learning to tailor sketching algorithms to the specific data distribution they are being run on. In particular, their learning-augmented frequency estimation algorithm uses a learned heavy-hitter oracle which predicts which elements will appear many times in the stream. We give a novel algorithm, which in some parameter regimes, already theoretically outperforms the learning based algorithm of Hsu et al. *without* the use of any predictions. Augmenting our algorithm with heavy-hitter predictions further reduces the error and improves upon the state of the art. Empirically, our algorithms achieve superior performance in all experiments compared to prior approaches.

Submission Number: 13335