Sampling Big Ideas in Sublinear Algorithms

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United States Simons Institute for the Theory of Computing
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Published on ● Video Link: https://www.youtube.com/watch?v=UtrnbXI1sK0


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Edith Cohen (Tel Aviv University and Google)
https://simons.berkeley.edu/talks/edith-cohen-tel-aviv-university-google-2024-05-21
Sublinear Algorithms Boot Camp

The use of random sampling can greatly enhance the scalability of complex data analysis tasks. Samples serve as versatile summaries that can be applied directly or integrated as a component in the data analysis process. In this talk, I will review some of my favorite big ideas in the design and applications of weighted and coordinated sampling schemes. The tutorial will particularly emphasize algorithmic simplicity and practicality and the context of streamed or distributed data.



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Tags:
Simons Institute
theoretical computer science
UC Berkeley
Computer Science
Theory of Computation
Theory of Computing
Sublinear Algorithms Boot Camp
Edith Cohen