Calibrated ensembles can mitigate accuracy tradeoffs under distribution shift
This addresses the problem of accuracy tradeoffs under distribution shift for machine learning practitioners, offering a simple and effective solution to improve robustness without sacrificing in-distribution performance.
The paper tackled the tradeoff between in-distribution (ID) and out-of-distribution (OOD) accuracy in robust machine learning by proposing ID-calibrated ensembles, which outperformed prior state-of-the-art methods on both ID and OOD accuracy across eleven natural distribution shift datasets.
We often see undesirable tradeoffs in robust machine learning where out-of-distribution (OOD) accuracy is at odds with in-distribution (ID) accuracy: a robust classifier obtained via specialized techniques such as removing spurious features often has better OOD but worse ID accuracy compared to a standard classifier trained via ERM. In this paper, we find that ID-calibrated ensembles -- where we simply ensemble the standard and robust models after calibrating on only ID data -- outperforms prior state-of-the-art (based on self-training) on both ID and OOD accuracy. On eleven natural distribution shift datasets, ID-calibrated ensembles obtain the best of both worlds: strong ID accuracy and OOD accuracy. We analyze this method in stylized settings, and identify two important conditions for ensembles to perform well both ID and OOD: (1) we need to calibrate the standard and robust models (on ID data, because OOD data is unavailable), (2) OOD has no anticorrelated spurious features.