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We consider data in the form of pairwise comparisons of n items, with the goal of precisely identifying the top k items for some value of k < n, or alternatively, recovering a ranking of all the items.
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Efficient computation of frequent and top-k elements in data streams
A. Metwally, D. Agrawal, and A. El Abbadi · 2005
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Klee: A framework for distributed top-k query algorithms
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Finding and approximating top-k answers in keyword proximity search
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A survey of top-k query processing techniques in relational database systems
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Learning to top-k search using pairwise comparisons
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Information complexity in bandit subset selection
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Efficient ranking from pairwise comparisons
F. Wauthier, M. Jordan, and N. Jojic · 2013
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Matrix estimation by universal singular value thresholding
S. Chatterjee · 2014
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Minimax-optimal inference from partial rankings
B. Hajek, S. Oh, and J. Xu · 2014
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A statistical convergence perspective of algorithms for rank aggregation from pairwise data
A. Rajkumar and S. Agarwal · 2014
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Introduction to Nonparametric Estimation
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Elements of information theory
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Iterative ranking from pair-wise comparisons
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Computing parametric ranking models via rank-breaking
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Spectral MLE: Top- k k rank aggregation from pairwise comparisons
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Stochastically transitive models for pairwise comparisons: Statistical and computational issues
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