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Conference Paper

Backward-Compatible Prediction Updates: A Probabilistic Approach

MPS-Authors
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Träuble,  Frederik
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

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

External Resource

https://proceedings.neurips.cc/paper_files/paper/2021/hash/012d9fe15b2493f21902cd55603382ec-Abstract.html
(Publisher version)

https://doi.org/10.48550/arXiv.2107.01057
(Preprint)

https://openreview.net/forum?id=YjZoWjTKYvH
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Citation

Träuble, F., von Kügelgen, J., Kleindessner, M., Locatello, F., Schölkopf, B., & Gehler, P. (2022). Backward-Compatible Prediction Updates: A Probabilistic Approach. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. Liang, & J. Wortman Vaughan (Eds.), Advances in Neural Information Processing Systems 34 (NeurIPS 2021) (pp. 116-128). Red Hook, NY: Curran Associates, Inc. Retrieved from https://proceedings.neurips.cc/paper_files/paper/2021/hash/012d9fe15b2493f21902cd55603382ec-Abstract.html.


Cite as: https://hdl.handle.net/21.11116/0000-000F-EB80-7
Abstract
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