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Out-of-distribution (OOD) data poses serious challenges in deployed machine learning models, so methods of predicting a model's performance on OOD data without labels are important for machine learning safety.
Improving predictive inference under covariate shift by weighting the log-likelihood function
Hidetoshi Shimodaira · 2000
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Optimal transport: old and new , volume 338
Cédric Villani et al · 2009
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Impossibility theorems for domain adaptation
Shai Ben David, Tyler Lu, Teresa Luu, and Dávid Pál · 2010
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Unsupervised supervised learning i: Estimating classification and regression errors without labels
Pinar Donmez, Guy Lebanon, and Krishnakumar Balasubramanian · 2010
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Unsupervised supervised learning ii: Margin-based classification without labels
Krishnakumar Balasubramanian, Pinar Donmez, and Guy Lebanon · 2011
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Robust statistics
Peter J Huber · 2011
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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Wasserstein generative adversarial networks
Martin Arjovsky, Soumith Chintala, and Léon Bottou · 2017
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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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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2018
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Benchmarking neural network robustness to common corruptions and perturbations
Dan Hendrycks and Thomas Dietterich · 2018
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Detecting and correcting for label shift with black box predictors
Zachary Lipton, Yu-Xiang Wang, and Alexander Smola · 2018
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Do cifar-10 classifiers generalize to cifar-10?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2018
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Normalized wasserstein for mixture distributions with applications in adversarial learning and domain adaptation
Yogesh Balaji, Rama Chellappa, and Soheil Feizi · 2019
Language-conditioned imitation learning for robot manipulation tasks
Simon Stepputtis, Joseph Campbell, Mariano Phielipp, Stefan Lee, Chitta Baral, and Heni Ben Amor · 2020
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Mandoline: Model evaluation under distribution shift
Mayee Chen, Karan Goel, Nimit S Sohoni, Fait Poms, Kayvon Fatahalian, and Christopher Ré · 2021
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Are labels always necessary for classifier accuracy evaluation?
Weijian Deng and Liang Zheng · 2021
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What does rotation prediction tell us about classifier accuracy under varying testing environments?
Weijian Deng, Stephen Gould, 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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Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, David Sculley, Sebastian Nowozin, Joshua Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
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Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al · 2019
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Computational optimal transport: With applications to data science
Gabriel Peyré, Marco Cuturi, et al · 2019
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Do imagenet classifiers generalize to imagenet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2019
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2019
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Learning robust global representations by penalizing local predictive power
Haohan Wang, Songwei Ge, Zachary Lipton, and Eric P Xing · 2019
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Geometric dataset distances via optimal transport
David Alvarez-Melis and Nicolo Fusi · 2020
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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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Slosh: Set locality sensitive hashing via sliced-wasserstein embeddings
Yuzhe Lu, Xinran Liu, Andrea Soltoggio, and Soheil Kolouri · 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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Wasserstein task embedding for measuring task similarities
Xinran Liu, Yikun Bai, Yuzhe Lu, Andrea Soltoggio, and Soheil Kolouri · 2022
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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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Agreement-on-the-line: Predicting the performance of neural networks under distribution shift, 2023
Christina Baek, Yiding Jiang, Aditi Raghunathan, and Zico Kolter · 2023
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Rlsbench: Domain adaptation under relaxed label shift
Saurabh Garg, Nick Erickson, James Sharpnack, Alex Smola, Sivaraman Balakrishnan, and Zachary Lipton · 2023
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