• Corpus ID: 182952827

Stretching the Effectiveness of MLE from Accuracy to Bias for Pairwise Comparisons

@inproceedings{Wang2019StretchingTE,
  title={Stretching the Effectiveness of MLE from Accuracy to Bias for Pairwise Comparisons},
  author={Jingyan Wang and Nihar B. Shah and Ramamoorthi Ravi},
  booktitle={International Conference on Artificial Intelligence and Statistics},
  year={2019}
}
A number of applications (e.g., AI bot tournaments, sports, peer grading, crowdsourcing) use pairwise comparison data and the Bradley-Terry-Luce (BTL) model to evaluate a given collection of items (e.g., bots, teams, students, search results). Past work has shown that under the BTL model, the widely-used maximum-likelihood estimator (MLE) is minimax-optimal in estimating the item parameters, in terms of the mean squared error. However, another important desideratum for designing estimators is… 

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