Publikation:

End-to-End Learning for Stochastic Optimization : a Bayesian Perspective

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2023

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Rychener, Yves
Kuhn, Daniel

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Proceedings of the 40th International Conference on Machine Learning. PMLR, 2023, S. 29455-29472. Proceedings of Machine Learning Research. 202. ISSN 2640-3498

Zusammenfassung

We develop a principled approach to end-to-end learning in stochastic optimization. First, we show that the standard end-to-end learning algorithm admits a Bayesian interpretation and trains a posterior Bayes action map. Building on the insights of this analysis, we then propose new end-to-end learning algorithms for training decision maps that output solutions of empirical risk minimization and distributionally robust optimization problems, two dominant modeling paradigms in optimization under uncertainty. Numerical results for a synthetic newsvendor problem illustrate the key differences between alternative training schemes. We also investigate an economic dispatch problem based on real data to showcase the impact of the neural network architecture of the decision maps on their test performance.

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004 Informatik

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40th International Conference on Machine Learning, 23. Juli 2023 - 29. Juli 2023, Honolulu, Hawaii
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ISO 690RYCHENER, Yves, Daniel KUHN, Tobias SUTTER, 2023. End-to-End Learning for Stochastic Optimization : a Bayesian Perspective. 40th International Conference on Machine Learning. Honolulu, Hawaii, 23. Juli 2023 - 29. Juli 2023. In: Proceedings of the 40th International Conference on Machine Learning. PMLR, 2023, S. 29455-29472. Proceedings of Machine Learning Research. 202. ISSN 2640-3498
BibTex
@inproceedings{Rychener2023Endto-69416,
  year={2023},
  title={End-to-End Learning for Stochastic Optimization : a Bayesian Perspective},
  url={https://proceedings.mlr.press/v202/rychener23a.html},
  number={202},
  issn={2640-3498},
  publisher={PMLR},
  series={Proceedings of Machine Learning Research},
  booktitle={Proceedings of the 40th International Conference on Machine Learning},
  pages={29455--29472},
  author={Rychener, Yves and Kuhn, Daniel and Sutter, Tobias}
}
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2024-02-28

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