Estimating Generalization under Distribution Shifts via Domain-Invariant Representations
This addresses the issue of overestimated performance in deployed models for practitioners dealing with distribution shifts, though it is incremental as it builds on existing domain-invariant representation methods.
The paper tackles the problem of estimating machine learning model performance under distribution shifts without supervision, using domain-invariant predictors as proxies, and shows that the approach enables self-tuning and accurately estimates target error.
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.