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arXiv · 1705.05366

Maximum Selection and Ranking under Noisy Comparisons

Moein Falahatgar·Alon Orlitsky·Venkatadheeraj Pichapati·Ananda Theertha Suresh
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Abstract

We consider $(\epsilon,\delta)$-PAC maximum-selection and ranking for general probabilistic models whose comparisons probabilities satisfy strong stochastic transitivity and stochastic triangle inequality. Modifying the popular knockout tournament, we propose a maximum-selection algorithm that uses $\mathcal{O}\left(\frac{n}{\epsilon^2}\log \frac{1}{\delta}\right)$ comparisons, a number tight up to a constant factor. We then derive a general framework that improves the performance of many ranking algorithms, and combine it with merge sort and binary search to obtain a ranking algorithm that uses $\mathcal{O}\left(\frac{n\log n (\log \log n)^3}{\epsilon^2}\right)$ comparisons for any $\delta\ge\frac1n$, a number optimal up to a $(\log \log n)^3$ factor.

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Moein Falahatgar, Alon Orlitsky, Venkatadheeraj Pichapati, Ananda Theertha Suresh. 2017-05-15. Maximum Selection and Ranking under Noisy Comparisons. https://arxiv.org/abs/1705.05366

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