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Real-world machine learning deployments are characterized by mismatches between the source (training) and target (test) distributions that may cause performance drops.
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Gintare Karolina Dziugaite and Daniel M Roy · 2017
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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
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2017
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Behnam Neyshabur, Srinadh Bhojanapalli, David McAllester, and Nathan Srebro · 2017
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Estimating accuracy from unlabeled data: A probabilistic logic approach
Emmanouil A Platanios, Hoifung Poon, Tom M Mitchell, and Eric Horvitz · 2017
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Functional map of the world
Gordon Christie, Neil Fendley, James Wilson, and Ryan Mukherjee · 2018
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Heinrich Jiang, Been Kim, Melody Y Guan, and Maya R Gupta · 2018
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Zachary C Lipton, Yu-Xiang Wang, and Alex Smola · 2018
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Rxrx1: An image set for cellular morphological variation across many experimental batches
J. Taylor, B. Earnshaw, B. Mabey, M. Victors, and J. Yosinski · 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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Chhavi Yadav and Léon Bottou · 2019
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Estimating generalization under distribution shifts via domain-invariant representations
Ching-Yao Chuang, Antonio Torralba, and Stefanie Jegelka · 2020
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A unified view of label shift estimation
Saurabh Garg, Yifan Wu, Sivaraman Balakrishnan, and Zachary C Lipton · 2020
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Understanding the failure modes of out-of-distribution generalization
Vaishnavh Nagarajan, Anders Andreassen, and Behnam Neyshabur · 2020
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Breeds: Benchmarks for subpopulation shift
Shibani Santurkar, Dimitris Tsipras, and Aleksander Madry · 2020
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Domain adaptation with conditional distribution matching and generalized label shift
Remi Tachet des Combes, Han Zhao, Yu-Xiang Wang, and Geoffrey J Gordon · 2020
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Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander M. Rush · 2020
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Hybrid models for open set recognition
Hongjie Zhang, Ang Li, Jie Guo, and Yanwen Guo · 2020
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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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Ratt: Leveraging unlabeled data to guarantee generalization
Saurabh Garg, Sivaraman Balakrishnan, J Zico Kolter, and Zachary C Lipton · 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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The many faces of robustness: A critical analysis of out-of-distribution generalization
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Predicting unreliable predictions by shattering a neural network
Xu Ji, Razvan Pascanu, Devon Hjelm, Andrea Vedaldi, Balaji Lakshminarayanan, and Yoshua Bengio · 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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WILDS: A benchmark of in-the-wild distribution shifts
Pang Wei Koh, Shiori Sagawa, Henrik Marklund, Sang Michael Xie, Marvin Zhang, Akshay Balsubramani, Weihua Hu, Michihiro Yasunaga, Richard Lanas Phillips, Irena Gao, Tony Lee, Etienne David, Ian Stavness, Wei Guo, Berton A. Earnshaw, Imran S. Haque, Sara Beery, Jure Leskovec, Anshul Kundaje, Emma Pierson, Sergey Levine, Chelsea Finn, and Percy Liang · 2021
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