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Sampling-Based Winner Prediction in District-Based Elections
We complement this result by proving that any algorithm, from a natural class of algorithms, for predicting the winner in a district-based election when $r$ is plurality, must sample at least $\Omega\left(\frac{1}{\varepsilon^4}\log\frac{1}{\delta}\right)$ votes.
Feature-based Individual Fairness in k-Clustering
Ensuring fairness in machine learning algorithms is a challenging and essential task.
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