2014
COLT
COLT 2014
Bayes-Optimal Scorers for Bipartite Ranking
Abstract
We address the following seemingly simple question: what is the Bayes-optimal scorer for a bipartite ranking risk? The answer to this question helps establish the consistency of the minimisation of surrogate bipartite risks, and elucidates the relationship between bipartite ranking and other established learning problems. We show that the answer is non-trivial in general, but may be easily determined for certain special cases using the theory of proper losses. Our analysis immediately establishes equivalences between several seemingly disparate risks for bipartite ranking, such as minimising a suitable class-probability estimation risk, and minimising the p-norm push risk proposed by Rudin (2009).
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Keyword Pioneer
— surrogate risk
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Cross-Pollinator
— Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
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Hot Topic Early Bird
— probabilistic modeling
Authors
Topics
Machine Learning > Core Methods > Classification
Machine Learning > Bayesian & Probabilistic > Bayesian Learning
Machine Learning > Core Methods > Probabilistic Modeling
Machine Learning > Bayesian & Probabilistic > Bayesian Inference
Machine Learning > Core Methods > Ranking
Machine Learning > Learning Types > Classification