Tight Bounds for Adversarially Robust Streams and Sliding Windows via Difference Estimators-Reference-Cited by-同舟云学术

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Tight Bounds for Adversarially Robust Streams and Sliding Windows via Difference Estimators

Published:2022-02 Issue: Volume: Page:
ISSN:
Container-title:2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS)
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Author:

Woodruff David P.¹,Zhou Samson¹

Affiliation:

1. Carnegie Mellon University

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9719594/9719683/09719698.pdf?arnumber=9719698

Reference37 articles.

1. Bloom Filters in Adversarial Environments

2. 1-Pass Relative-Error Lp-Sampling with Applications

3. Non-adaptive adaptive sampling on turnstile streams

4. One-dimensional stable distributions;zolotarev;Bull Amer Math Soc,1989

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2. The Complexity of Dynamic Least-Squares Regression;2023 IEEE 64th Annual Symposium on Foundations of Computer Science (FOCS);2023-11-06

3. Adversarially Robust Streaming Algorithms via Differential Privacy;Journal of the ACM;2022-11-24

4. Almost-Smooth Histograms and Sliding-Window Graph Algorithms;Algorithmica;2022-06-24

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