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Epsilon-approximations and epsilon-nets
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abstract
The use of random samples to approximate properties of geometric configurations has been an influential idea for both combinatorial and algorithmic purposes. This chapter considers two related notions---$\epsilon$-approximations and $\epsilon$-nets---that capture the most important quantitative properties that one would expect from a random sample with respect to an underlying geometric configuration.
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Cited by 1 Pith paper
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The Adversarial Robustness of Sketching and Streaming Algorithms
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