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Wilds: A benchmark of in-the-wild distribution shifts, , , , , , , , , and 1 other author(s). International Conference on Machine Learning, page 5637--5664. PMLR, (2021)An Explanation of In-context Learning as Implicit Bayesian Inference., , , and . CoRR, (2021)Data Selection for Language Models via Importance Resampling., , , and . CoRR, (2023)Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance., , and . NeurIPS, page 5327-5338. (2018)Understanding and Mitigating the Tradeoff between Robustness and Accuracy., , , , and . ICML, volume 119 of Proceedings of Machine Learning Research, page 7909-7919. PMLR, (2020)Same Pre-training Loss, Better Downstream: Implicit Bias Matters for Language Models., , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 22188-22214. PMLR, (2023)Transfer Learning from Deep Features for Remote Sensing and Poverty Mapping., , , , and . AAAI, page 3929-3935. AAAI Press, (2016)WILDS: A Benchmark of in-the-Wild Distribution Shifts., , , , , , , , , and 6 other author(s). CoRR, (2020)Composed Fine-Tuning: Freezing Pre-Trained Denoising Autoencoders for Improved Generalization., , and . ICML, volume 139 of Proceedings of Machine Learning Research, page 11424-11435. PMLR, (2021)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)