High Probability Frequency Moment Sketches

TL;DR

This paper establishes tight bounds for high-confidence frequency moment sketches for p>2, showing that naive repetition methods are suboptimal and providing optimal algorithms and bounds.

Contribution

It introduces the first tight bounds for high-confidence frequency moment sketches for p>2, demonstrating the suboptimality of simple repetition methods and providing optimal algorithms.

Findings

Optimal upper and lower bounds on sketching dimension for p>2.

Repetition with median is suboptimal for high-confidence frequency moments.

Matching lower bounds up to constant factors.

Abstract

We consider the problem of sketching the $p$-th frequency moment of a vector, $p>2$, with multiplicative error at most $1\pm \epsilon$ and \emph{with high confidence} $1-\delta$. Despite the long sequence of work on this problem, tight bounds on this quantity are only known for constant $\delta$. While one can obtain an upper bound with error probability $\delta$ by repeating a sketching algorithm with constant error probability $O(\log(1/\delta))$ times in parallel, and taking the median of the outputs, we show this is a suboptimal algorithm! Namely, we show optimal upper and lower bounds of $\Theta(n^{1-2/p} \log(1/\delta) + n^{1-2/p} \log^{2/p} (1/\delta) \log n)$ on the sketching dimension, for any constant approximation. Our result should be contrasted with results for estimating frequency moments for $1 \leq p \leq 2$, for which we show the optimal algorithm for general $\delta$ is obtained by repeating the optimal algorithm for constant error probability $O(\log(1/\delta))$ times and taking the median output. We also obtain a matching lower bound for this problem, up to constant factors.