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Out-of-distribution (OOD) data poses serious challenges in deployed machine learning models as even subtle changes could incur significant performance drops.
Impossibility theorems for domain adaptation
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
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Estimating accuracy from unlabeled data: A bayesian approach
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Estimating accuracy from unlabeled data: A probabilistic logic approach
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Zachary Lipton, Yu-Xiang Wang, and Alexander Smola · 2018
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Energy-based out-of-distribution detection
Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li · 2020
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Are labels always necessary for classifier accuracy evaluation?
Weijian Deng and Liang Zheng · 2021
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Pot: Python optimal transport
Rémi Flamary, Nicolas Courty, Alexandre Gramfort, Mokhtar Z. Alaya, Aurélie Boisbunon, Stanislas Chambon, Laetitia Chapel, Adrien Corenflos, Kilian Fatras, Nemo Fournier, Léo Gautheron, Nathalie T.H. Gayraud, Hicham Janati, Alain Rakotomamonjy, Ievgen Redko, Antoine Rolet, Antony Schutz, Vivien Seguy, Danica J. Sutherland, Romain Tavenard, Alexander Tong, and Titouan Vayer · 2021
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Predicting with confidence on unseen distributions
Devin Guillory, Vaishaal Shankar, Sayna Ebrahimi, Trevor Darrell, and Ludwig Schmidt · 2021
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Assessing generalization of sgd via disagreement
Yiding Jiang, Vaishnavh Nagarajan, Christina Baek, and J Zico Kolter · 2021
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Leveraging unlabeled data to predict out-of-distribution performance
Saurabh Garg, Sivaraman Balakrishnan, Zachary C Lipton, Behnam Neyshabur, and Hanie Sedghi · 2022
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Dequan Wang, Evan Shelhamer, Shaoteng Liu, Bruno Olshausen, and Trevor Darrell · 2020
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Agreement-on-the-line: Predicting the performance of neural networks under distribution shift
Christina Baek, Yiding Jiang, Aditi Raghunathan, and J Zico Kolter
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Predicting out-of-distribution error with the projection norm
Yaodong Yu, Zitong Yang, Alexander Wei, Yi Ma, and Jacob Steinhardt · 2022
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