9 citations · 12 across the 3 of their papers we have counts for
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stat.ML2019★ 9 cited
Structured Prediction with Projection Oracles
Mathieu Blondel
We propose in this paper a general framework for deriving loss functions for structured prediction. In our framework, the user chooses a convex set including the output space and p…
stat.ML2019★ 3 cited
Geometric Losses for Distributional Learning
Arthur Mensch, Mathieu Blondel, Gabriel Peyré
Building upon recent advances in entropy-regularized optimal transport, and upon Fenchel duality between measures and continuous functions , we propose a generalization of the logi…
stat.ML2019
Learning with Fenchel-Young Losses
Mathieu Blondel, André F. T. Martins, Vlad Niculae
Over the past decades, numerous loss functions have been been proposed for a variety of supervised learning tasks, including regression, classification, ranking, and more generally…