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Hybrid-MST: A Hybrid Active Sampling Strategy for Pairwise Preference Aggregation
JING LI · Rafal Mantiuk · Junle Wang · Suiyi Ling · Patrick Le Callet

Thu Dec 06 02:00 PM -- 04:00 PM (PST) @ Room 517 AB #126

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 labeling. 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. For computational efficiency, Gaussian-Hermite quadrature is used for estimation of EIG. In this work, a hybrid active sampling strategy is proposed, either using Global Maximum (GM) EIG sampling or Minimum Spanning Tree (MST) sampling in each trial, which is determined by the test budget. The proposed method has been validated on both simulated and real-world datasets, where it shows higher preference aggregation ability than the state-of-the-art methods.

Author Information

JING LI (University of Nantes, LS2N lab)
Rafal Mantiuk (University of Cambridge)
Junle Wang (Tencent)
Suiyi Ling (Capacites)
Patrick Le Callet ("Universite de Nantes, France")