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Out-of-Domain Robustness via Targeted Augmentations.

, , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 10800-10834. PMLR, (2023)

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@ CREPE: Can Vision-Language Foundation Models Reason Compositionally?, , , , , and . CVPR, page 10910-10921. IEEE, (2023)Extending the WILDS Benchmark for Unsupervised Adaptation., , , , , , , , , and 10 other author(s). ICLR, OpenReview.net, (2022)Extending the WILDS Benchmark for Unsupervised Adaptation., , , , , , , , , and 10 other author(s). CoRR, (2021)Effect of Confidence Indicators on Trust in AI-Generated Profiles., , , , and . CHI Extended Abstracts, page 1-8. ACM, (2020)Wilds: A benchmark of in-the-wild distribution shifts, , , , , , , , , and 1 other author(s). International Conference on Machine Learning, page 5637--5664. PMLR, (2021)Fair Matching in Dynamic Kidney Exchange.. CoRR, (2019)CREPE: Can Vision-Language Foundation Models Reason Compositionally?, , , , , and . CoRR, (2022)OpenFlamingo: An Open-Source Framework for Training Large Autoregressive Vision-Language Models., , , , , , , , , and 6 other author(s). CoRR, (2023)WILDS: A Benchmark of in-the-Wild Distribution Shifts., , , , , , , , , and 13 other author(s). ICML, volume 139 of Proceedings of Machine Learning Research, page 5637-5664. PMLR, (2021)Adaptive Testing of Computer Vision Models., , , and . CoRR, (2022)