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Etienne David

2 accepted papers

2022

Extending the WILDS Benchmark for Unsupervised Adaptation

ICLR 2022oral

Machine learning systems deployed in the wild are often trained on a source distribution but deployed on a different target distribution. Unlabeled data can be a powerful point of leverage for mitigating these distribution shifts, as it is frequently much more available than labeled data and can oft…

Cited by 143SourcePDFScholar
2021

WILDS: A Benchmark of in-the-Wild Distribution Shifts

ICML 2021oral

Distribution shifts—where the training distribution differs from the test distribution—can substantially degrade the accuracy of machine learning (ML) systems deployed in the wild. Despite their ubiquity in the real-world deployments, these distribution shifts are under-represented in the datasets w…