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Conference Paper

Probable Domain Generalization via Quantile Risk Minimization

MPS-Authors

Eastwood ,  Cian
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;
External Organizations;

Singh,  Shashank
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons249656

von Kügelgen,  Julius
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;
External Organizations;

/persons/resource/persons84193

Schölkopf,  Bernhard       
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

External Resource

https://proceedings.neurips.cc/paper_files/paper/2022/hash/6f11132f6ecbbcafafdf6decfc98f7be-Abstract-Conference.html
(Publisher version)

https://openreview.net/forum?id=6FkSHynJr1
(Any fulltext)

https://doi.org/10.48550/arXiv.2207.09944
(Preprint)

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Citation

Eastwood, C., Robey, A., Singh, S., von Kügelgen, J., Hassani, H., Pappas, G. J., et al. (2022). Probable Domain Generalization via Quantile Risk Minimization. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, & A. Oh (Eds.), Advances in Neural Information Processing Systems 35 (NeurIPS 2022) (pp. 17340-17358). Red Hook, NY: Curran Associates, Inc. Retrieved from https://proceedings.neurips.cc/paper_files/paper/2022/hash/6f11132f6ecbbcafafdf6decfc98f7be-Abstract-Conference.html.


Cite as: https://hdl.handle.net/21.11116/0000-000F-EC64-7
Abstract
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