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Learning to predict multi-label outputs is challenging, but in many problems there is a natural metric on the outputs that can be used to improve predictions.
A class of wasserstein metrics for probability distributions
Clark R. Givens and Rae Michael Shortt · 1984
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Nonlinear total variation based noise removal algorithms
Leonid I Rudin, Stanley Osher, and Emad Fatemi · 1992
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Introduction to Linear Optimization
Dimitris Bertsimas, John N. Tsitsiklis, and John Tsitsiklis · 1997
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The earth mover’s distance as a metric for image retrieval
Yossi Rubner, Carlo Tomasi, and Leonidas J Guibas · 2000
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Rademacher and gaussian complexities: Risk bounds and structural results
Peter L Bartlett and Shahar Mendelson · 2003
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On minimum kantorovich distance estimators
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S Shirdhonkar and D W Jacobs · 2008
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Semantic image segmentation with deep convolutional nets and fully connected crfs
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