network traffic analysts

Abstract: Score-based generative models driven by $α$-stable L{é}vy processes have been proposed as an alternative to Brownian diffusion models, with empirical evidence suggesting improved sampling and better modeling of heavy-tailed data. Yet theoretical guarantees for these models remain limited. We develop a non-asymptotic convergence analysis for scale-preserving $α$-stable generative dynamics, covering both compactly supported and power-law targets. In p-Wasserstein distance, we quantify the effects of early stopping, initialization, score approximation, and time discretization, and show exponential decay of the initialization error. A comparison with Brownian dynamics reveals that global Wasserstein convergence alone does not fundamentally distinguish the two models: both initialization errors decay exponentially with the forward horizon on the common class of heavy-tailed targets. A sharp separation emerges instead in the tails. At any positive generation time, stable dynamics produces power-law tails with index $α$, whereas Brownian dynamics remains light-tailed. For a target with tail index $ν$, asymptotically correct extreme-quantile scaling is possible precisely when $α$=$ν$. These results identify tail-index matching as a principled mechanism behind the ability of stable generative models to capture extreme events.

Abstract: Estimating the second frequency moment ($F_2$) of an underlying frequency vector is a fundamental problem in the streaming model. While recent work by Braverman and Zamir [STOC 2025] resolved the space complexity for constant failure probability in the insertion-only model, the optimal dependence on the failure parameter $δ$ remained open. We close this gap by proving a tight high-probability lower bound of $Ω\left(\frac{1}{\varepsilon^2}\log\frac{1}δ\,\log\frac{\varepsilon\sqrt{n}}{\log(1/δ)}\right)$ for $(1\pm\varepsilon)$-approximate $F_2$ estimation. The key challenge is the failure of prior multi-scale direct sum arguments under noise sensitivity. We introduce a noise-robust communication primitive, Exam Mostly Set Disjointness, and prove an $Ω\left(\frac{m}{t}\log\frac{1}δ\right)$ one-way lower bound. Embedding this into a multi-scale reduction yields the correct $\log(1/δ)$ dependence. We also give two complementary algorithms under natural structure assumptions. For streams with frequency bound $B$, we design a subsampling method using continuous $F_0$ tracking that replaces a $\log(n)$ factor with $\text{polylog}(B)$. For $k$-sparse streams, we develop a two-stage sketch using approximate Morris counters, replacing $\log n$ with $\log k$ and achieving a further $\log\log m$ dependence on stream length.