Honghao Lin

activity

20202026

most citedLearning Augmented Binary Search Trees

3 citations · 5 across the 21 of their papers we have counts for

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Showing 2025Show all

4 papers · 1 filter

cs.DS2025

$L_{p}$ Sampling in Distributed Data Streams with Applications to Adversarial Robustness

Honghao Lin, Zhao Song, David P. Woodruff +2

In the distributed monitoring model, a data stream over a universe of size $n$ is distributed over $k$ servers, who must continuously provide certain statistics of the overall data…

cs.DS2025

Unbiased Insights: Optimal Streaming Algorithms for $ℓ_{p}$ Sampling, the Forget Model, and Beyond

Honghao Lin, Hoai-An Nguyen, William Swartworth +1

We study $ℓ_{p}$ sampling and frequency moment estimation in a single-pass insertion-only data stream. For $p \in (0, 2)$ , we present a nearly space-optimal approximate $ℓ_{p}$ sa…

cs.DS2025

On Sketching Trimmed Statistics

Honghao Lin, Hoai-An Nguyen, David P. Woodruff

We study sketching trimmed statistics of a frequency vector, including the $F_{p}$ moment of the top- $k$ coordinates and of the trimmed- $k$ vector. Despite their natural role in robu…

cs.DS2025

Lifting Linear Sketches: Optimal Bounds and Adversarial Robustness

Elena Gribelyuk, Honghao Lin, David P. Woodruff +2

We introduce a novel technique for ``lifting'' dimension lower bounds for linear sketches in the real-valued setting to dimension lower bounds for linear sketches with polynomially…

Lp​ Sampling in Distributed Data Streams with Applications to Adversarial Robustness

Unbiased Insights: Optimal Streaming Algorithms for ℓp​ Sampling, the Forget Model, and Beyond

On Sketching Trimmed Statistics

Lifting Linear Sketches: Optimal Bounds and Adversarial Robustness

$L_{p}$ Sampling in Distributed Data Streams with Applications to Adversarial Robustness

Unbiased Insights: Optimal Streaming Algorithms for $ℓ_{p}$ Sampling, the Forget Model, and Beyond