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- W3114130775 abstract "When machine learning models are deployed on a test distribution different from the training distribution, they can perform poorly, but overestimate their performance. In this work, we aim to better estimate a model's performance under distribution shift, without supervision. To do so, we use a set of domain-invariant predictors as a proxy for the unknown, true target labels. Since the error of the resulting risk estimate depends on the target risk of the proxy model, we study generalization of domain-invariant representations and show that the complexity of the latent representation has a significant influence on the target risk. Empirically, our approach (1) enables self-tuning of domain adaptation models, and (2) accurately estimates the target error of given models under distribution shift. Other applications include model selection, deciding early stopping and error detection." @default.
- W3114130775 created "2021-01-05" @default.
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- W3114130775 date "2020-11-21" @default.
- W3114130775 modified "2023-09-26" @default.
- W3114130775 title "Estimating Generalization under Distribution Shifts via Domain-Invariant Representations" @default.
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