Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation

NeurIPS 2018 Jing LiRafal K. MantiukJunle WangSuiyi LingPatrick Le Callet

In this paper we present a hybrid active sampling strategy for pairwise preference aggregation, which aims at recovering the underlying rating of the test candidates from sparse and noisy pairwise labelling. Our method employs Bayesian optimization framework and Bradley-Terry model to construct the utility function, then to obtain the Expected Information Gain (EIG) of each pair... (read more)

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