Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation

• URL: http://arxiv.org/abs/2504.11284v1
• Date: Tue, 15 Apr 2025 15:25:27 GMT
• Title: Bipartite Ranking From Multiple Labels: On Loss Versus Label Aggregation
• Authors: Michal Lukasik, Lin Chen, Harikrishna Narasimhan, Aditya Krishna Menon, Wittawat Jitkrittum, Felix X. Yu, Sashank J. Reddi, Gang Fu, Mohammadhossein Bateni, Sanjiv Kumar,
• Abstract summary: Bipartite ranking is a fundamental supervised learning problem, with the goal of learning a ranking over instances with maximal area under the ROC curve (AUC) against a single binary target label.<n>How can one synthesize such labels into a single coherent ranking?<n>We analyze two approaches to this problem -- loss aggregation and label aggregation -- by characterizing their Bayes-optimal solutions.
• Score: 66.28528968249255
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Bipartite ranking is a fundamental supervised learning problem, with the goal of learning a ranking over instances with maximal area under the ROC curve (AUC) against a single binary target label. However, one may often observe multiple binary target labels, e.g., from distinct human annotators. How can one synthesize such labels into a single coherent ranking? In this work, we formally analyze two approaches to this problem -- loss aggregation and label aggregation -- by characterizing their Bayes-optimal solutions. Based on this, we show that while both methods can yield Pareto-optimal solutions, loss aggregation can exhibit label dictatorship: one can inadvertently (and undesirably) favor one label over others. This suggests that label aggregation can be preferable to loss aggregation, which we empirically verify.

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