• Corpus ID: 211204882

Learning with Differentiable Perturbed Optimizers

@article{Berthet2020LearningWD,
  title={Learning with Differentiable Perturbed Optimizers},
  author={Quentin Berthet and Mathieu Blondel and Olivier Teboul and Marco Cuturi and Jean-Philippe Vert and Francis R. Bach},
  journal={ArXiv},
  year={2020},
  volume={abs/2002.08676}
}
Machine learning pipelines often rely on optimization procedures to make discrete decisions (e.g., sorting, picking closest neighbors, or shortest paths). Although these discrete decisions are easily computed, they break the back-propagation of computational graphs. In order to expand the scope of learning problems that can be solved in an end-to-end fashion, we propose a systematic method to transform optimizers into operations that are differentiable and never locally constant. Our approach… 
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