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Learning Output Embeddings in Structured Prediction

Abstract : A powerful and flexible approach to structured prediction consists in embedding the structured objects to be predicted into a feature space of possibly infinite dimension by means of output kernels, and then, solving a regression problem in this output space. A prediction in the original space is computed by solving a pre-image problem. In such an approach, the embedding, linked to the target loss, is defined prior to the learning phase. In this work, we propose to jointly learn a finite approximation of the output embedding and the regression function into the new feature space. For that purpose, we leverage a priori information on the outputs and also unexploited unsupervised output data, which are both often available in structured prediction problems. We prove that the resulting structured predictor is a consistent estimator, and derive an excess risk bound. Moreover, the novel structured prediction tool enjoys a significantly smaller computational complexity than former output kernel methods. The approach empirically tested on various structured prediction problems reveals to be versatile and able to handle large datasets.
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Preprints, Working Papers, ...
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Contributor : Céline Brouard Connect in order to contact the contributor
Submitted on : Friday, July 9, 2021 - 11:06:41 AM
Last modification on : Friday, September 23, 2022 - 4:48:06 PM

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  • HAL Id : hal-03282445, version 1
  • ARXIV : 2007.14703


Luc Brogat-Motte, Alessandro Rudi, Celine Brouard, Juho Rousu, Florence d'Alché-Buc. Learning Output Embeddings in Structured Prediction. 2021. ⟨hal-03282445⟩



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