Publikation:

Robust Generalization despite Distribution Shift via Minimum Discriminating Information

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2021

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Krause, Andreas
Kuhn, Daniel

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RANZATO, Marc'Aurelio, Hrsg., Alina BEYGELZIMER, Hrsg., Yann DAUPHIN, Hrsg. und andere. Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021). San Diego, CA: Neural Information Processing Systems Foundation, 2021

Zusammenfassung

Training models that perform well under distribution shifts is a central challenge in machine learning. In this paper, we introduce a modeling framework where, in addition to training data, we have partial structural knowledge of the shifted test distribution. We employ the principle of minimum discriminating information to embed the available prior knowledge, and use distributionally robust optimization to account for uncertainty due to the limited samples. By leveraging large deviation results, we obtain explicit generalization bounds with respect to the unknown shifted distribution. Lastly, we demonstrate the versatility of our framework by demonstrating it on two rather distinct applications: (1) training classifiers on systematically biased data and (2) off-policy evaluation in Markov Decision Processes.

Zusammenfassung in einer weiteren Sprache

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004 Informatik

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NeurIPS 2021 : 35th Conference on Neural Information Processing Systems (online), 6. Dez. 2021 - 14. Dez. 2021
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ISO 690SUTTER, Tobias, Andreas KRAUSE, Daniel KUHN, 2021. Robust Generalization despite Distribution Shift via Minimum Discriminating Information. NeurIPS 2021 : 35th Conference on Neural Information Processing Systems (online), 6. Dez. 2021 - 14. Dez. 2021. In: RANZATO, Marc'Aurelio, Hrsg., Alina BEYGELZIMER, Hrsg., Yann DAUPHIN, Hrsg. und andere. Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021). San Diego, CA: Neural Information Processing Systems Foundation, 2021
BibTex
@inproceedings{Sutter2021Robus-55736,
  year={2021},
  title={Robust Generalization despite Distribution Shift via Minimum Discriminating Information},
  url={https://proceedings.neurips.cc/paper/2021/hash/f86890095c957e9b949d11d15f0d0cd5-Abstract.html},
  publisher={Neural Information Processing Systems Foundation},
  address={San Diego, CA},
  booktitle={Advances in Neural Information Processing Systems 34 pre-proceedings (NeurIPS 2021)},
  editor={Ranzato, Marc'Aurelio and Beygelzimer, Alina and Dauphin, Yann},
  author={Sutter, Tobias and Krause, Andreas and Kuhn, Daniel}
}
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