15 citations · 22 across the 3 of their papers we have counts for
4 papers · 1 filter
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…
Learning Classifiers with Fenchel-Young Losses: Generalized Entropies, Margins, and Algorithms
Mathieu Blondel, André F. T. Martins, Vlad Niculae
This paper studies Fenchel-Young losses, a generic way to construct convex loss functions from a regularization function. We analyze their properties in depth, showing that they un…
SparseMAP: Differentiable Sparse Structured Inference
Vlad Niculae, André F. T. Martins, Mathieu Blondel +1
Structured prediction requires searching over a combinatorial number of structures. To tackle it, we introduce SparseMAP: a new method for sparse structured inference, and its natu…
Multi-output Polynomial Networks and Factorization Machines
Mathieu Blondel, Vlad Niculae, Takuma Otsuka +1
Factorization machines and polynomial networks are supervised polynomial models based on an efficient low-rank decomposition. We extend these models to the multi-output setting, i.…