Adversarial Robustness for Small Frequency Moments and a Weak Equivalence Theorem for Turnstile Streams

2026-07-07

AI Summary

This work addresses the problem of achieving $(1+\varepsilon)$-approximations for all frequency moments $F_p$ with $p \in [0,2]$—including $F_0$—in the adversarial insertion-deletion streaming model. Prior methods were limited to constant-factor approximations. By extending the estimator-corrector-learner framework to non-Hilbert spaces and combining implicit isometric embeddings into $L_2$ with regularized kernel ridge regression to handle adaptive queries, this paper presents the first algorithm that achieves $(1+\varepsilon)$-approximation using $\mathrm{poly}(1/\varepsilon, \log n)$ space, matching the optimal space complexity. Furthermore, the authors establish a weak equivalence between linear sketches and adversarially robust algorithms, revealing $L_1$ embeddability as a unifying principle, and demonstrate that the same framework enables efficient adversarially robust estimation of Earth Mover’s Distance (EMD), $k$-median, and Shannon entropy.

Abstract

We study adversarially robust algorithms for insertion-deletion (turnstile) streams, where future updates may depend on past algorithm outputs. While recent work achieved a robust $(1+ε)$-approximation for the second moment $F_2$ in polylogarithmic space, achieving high accuracy for other frequency moments remained a major open question; for $p\in[0,2)$, including the fundamental distinct elements problem ($F_0$), only constant-factor approximations were known in sublinear space. We close this gap, showing that $(1+ε)$-approximate robustness can be achieved in polylogarithmic space for all $p\in[0,2]$. Our approach generalizes the estimator-corrector-learner framework to non-Hilbert spaces by dynamically maintaining implicit isometric embeddings into $L_2$ and performing regularized kernel ridge regression over adaptively discovered hard queries, yielding the first insertion-deletion algorithms that approximate: (1) the $p$-th frequency moment $F_p$ up to a $(1+ε)$-factor in poly$(1/ε, \log n)$ space for all $p\in[0,2]$, including the support size $F_0$, (2) metric and information-theoretic quantities, including the Earth Mover Distance (EMD) and $k$-median clustering cost over $[Δ]^d$ up to an $O(d \log Δ)$-factor, and the Shannon entropy up to an $ε$-additive error, and (3) non-normed symmetric losses defined by Bernstein functions up to a $(1+ε)$-factor. For the $F_p$ moments, our algorithm is optimal up to poly$(1/ε, \log n)$ factors. Furthermore, we establish a weak equivalence between classical oblivious sketching and adversarial robustness. We prove that for any sub-multiplicative norm, the existence of an efficient classical linear sketch is equivalent to the existence of an efficient robust turnstile algorithm, up to polynomial factors, formalizing $L_1$ embeddability as the fundamental mechanism governing both models.

Problem

• adversarial robustness
• frequency moments
• turnstile streams
• distinct elements
• streaming algorithms

Innovation

• adversarial robustness
• frequency moments
• turnstile streams
• isometric embedding
• weak equivalence theorem