Truly Perfect Samplers for Data Streams and Sliding Windows

Author:

Jayaram Rajesh1,Woodruff David P.1,Zhou Samson1

Affiliation:

1. Carnegie Mellon University, Pittsburgh, PA, USA

Funder

National Science Foundation

Publisher

ACM

Reference76 articles.

1. Ankit Aggarwal , Amit Deshpande , and Ravi Kannan . 2009 . Adaptive Sampling for k-Means Clustering. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques , 12th International Workshop, APPROX and 13th International Workshop, RANDOM. Proceedings. 15--28 . Ankit Aggarwal, Amit Deshpande, and Ravi Kannan. 2009. Adaptive Sampling for k-Means Clustering. In Approximation, Randomization, and Combinatorial Optimization. Algorithms and Techniques, 12th International Workshop, APPROX and 13th International Workshop, RANDOM. Proceedings. 15--28.

2. The Space Complexity of Approximating the Frequency Moments

3. Streaming Algorithms via Precision Sampling

4. Idan Attias , Edith Cohen , Moshe Shechner , and Uri Stemmer . 2021. A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators. CoRR , Vol. abs/ 2107 .14527 ( 2021 ). Idan Attias, Edith Cohen, Moshe Shechner, and Uri Stemmer. 2021. A Framework for Adversarial Streaming via Differential Privacy and Difference Estimators. CoRR, Vol. abs/2107.14527 (2021).

5. Adversarially Robust Submodular Maximization under Knapsack Constraints

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