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20202026
most citedData-Independent Structured Pruning of Neural Networks via Coresets

4 citations · 6 across the 21 of their papers we have counts for

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18 papers · 1 filter

cs.DS2026

Distributed Algorithms for Euclidean Clustering

Vincent Cohen-Addad, Liudeng Wang, David P. Woodruff +1

We study the problem of constructing -coresets for Euclidean -clustering in the distributed setting, where data points are partitioned across sites.…

cs.DS2026

Learning-Augmented Moment Estimation on Time-Decay Models

Soham Nagawanshi, Shalini Panthangi, Chen Wang +2

Motivated by the prevalence and success of machine learning, a line of recent work has studied learning-augmented algorithms in the streaming model. These results have shown that f…

cs.DS2026

Consistent Low-Rank Approximation

David P. Woodruff, Samson Zhou

We introduce and study the problem of consistent low-rank approximation, in which rows of an input matrix arrive sequentially and the goal is…

cs.DS2026

Adversarial Robustness on Insertion-Deletion Streams

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

We study adversarially robust algorithms for insertion-deletion (turnstile) streams, where future updates may depend on past algorithm outputs. While robust algorithms exist for in…

cs.DS2025

Perfect Sampling with Polylogarithmic Update Time

William Swartworth, David P. Woodruff, Samson Zhou

Perfect sampling in a stream was introduced by Jayaram and Woodruff (FOCS 2018) as a streaming primitive which, given turnstile updates to a vector $x \in \{-\text{poly}(n),…

cs.DS2025

Nearly Space-Optimal Graph and Hypergraph Sparsification in Insertion-Only Data Streams

Vincent Cohen-Addad, David P. Woodruff, Shenghao Xie +1

We study the problem of graph and hypergraph sparsification in insertion-only data streams. The input is a hypergraph with nodes, hyperedges, and rank , an…