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The performance of machine learning models under distribution shift has been the focus of the community in recent years.
Why do we sometimes get nonsense-correlations between time-series?–a study in sampling and the nature of time-series
G Udny Yule · 1926
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Spurious correlation: A causal interpretation
Herbert A Simon · 1954
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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The nature of statistical learning theory
Vladimir Vapnik · 2013
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Unbiased metric learning: On the utilization of multiple datasets and web images for softening bias
Chen Fang, Ye Xu, and Daniel N Rockmore · 2013
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Deep domain confusion: Maximizing for domain invariance
Eric Tzeng, Judy Hoffman, Ning Zhang, Kate Saenko, and Trevor Darrell · 2014
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ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Unsupervised domain adaptation by backpropagation
Yaroslav Ganin and Victor Lempitsky · 2015
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
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Stochastic gradient methods for distributionally robust optimization with f-divergences
Hongseok Namkoong and John C Duchi · 2016
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, Eric Price, and Nati Srebro · 2016
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Causal inference using invariant prediction: identification and confidence intervals
J. Peters, P. Bühlmann, and N. Meinshausen · 2016
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, François Laviolette, Mario Marchand, and Victor Lempitsky · 2016
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Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2017
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Deeper, broader and artier domain generalization
Da Li, Yongxin Yang, Yi-Zhe Song, and Timothy M Hospedales · 2017
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Deep hashing network for unsupervised domain adaptation
Hemanth Venkateswara, Jose Eusebio, Shayok Chakraborty, and Sethuraman Panchanathan · 2017
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Densely connected convolutional networks
Gao Huang, Zhuang Liu, Laurens Van Der Maaten, and Kilian Q Weinberger · 2017
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2017
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Functional map of the world
Gordon Christie, Neil Fendley, James Wilson, and Ryan Mukherjee · 2018
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From detection of individual metastases to classification of lymph node status at the patient level: the camelyon17 challenge
Peter Bandi, Oscar Geessink, Quirine Manson, Marcory Van Dijk, Maschenka Balkenhol, Meyke Hermsen, Babak Ehteshami Bejnordi, Byungjae Lee, Kyunghyun Paeng, Aoxiao Zhong, et al · 2018
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Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 2018
Cited alongside, same era.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2018
Cited alongside, same era.
Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2019
Cited alongside, same era.
Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
Cited alongside, same era.
Distributionally robust language modeling
Yonatan Oren, Shiori Sagawa, Tatsunori B. Hashimoto, and Percy Liang · 2019
Cited alongside, same era.
Benchmarking neural network robustness to common corruptions and perturbations
Out-of-distribution generalization via risk extrapolation (rex)
David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville · 2021
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Learning models with uniform performance via distributionally robust optimization
John C Duchi and Hongseok Namkoong · 2021
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Heterogeneous risk minimization
Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, and Zheyan Shen · 2021
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Environment inference for invariant learning
Elliot Creager, Jörn-Henrik Jacobsen, and Richard Zemel · 2021
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Kernelized heterogeneous risk minimization
Jiashuo Liu, Zheyuan Hu, Peng Cui, Bo Li, and Zheyan Shen · 2021
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Regularizing towards causal invariance: Linear models with proxies
Michael Oberst, Nikolaj Thams, Jonas Peters, and David A. Sontag · 2021
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Dan Hendrycks and Thomas Dietterich · 2019
Cited alongside, same era.
Moment matching for multi-source domain adaptation
Xingchao Peng, Qinxun Bai, Xide Xia, Zijun Huang, Kate Saenko, and Bo Wang · 2019
Cited alongside, same era.
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
Cited alongside, same era.
An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, et al · 2020
Cited alongside, same era.
Invariant risk minimization games
Kartik Ahuja, Karthikeyan Shanmugam, Kush Varshney, and Amit Dhurandhar · 2020
Cited alongside, same era.
The risks of invariant risk minimization
Elan Rosenfeld, Pradeep Ravikumar, and Andrej Risteski · 2020
Cited alongside, same era.
An empirical study of invariant risk minimization
Yo Joong Choe, Jiyeon Ham, and Kyubyong Park · 2020
Cited alongside, same era.
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Natural adversarial examples
Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt, and Dawn Song · 2021
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Towards out-of-distribution generalization: A survey
Zheyan Shen, Jiashuo Liu, Yue He, Xingxuan Zhang, Renzhe Xu, Han Yu, and Peng Cui · 2021
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Breeds: Benchmarks for subpopulation shift
Shibani Santurkar, Dimitris Tsipras, and Aleksander Madry · 2021
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Just train twice: Improving group robustness without training group information
Evan Z Liu, Behzad Haghgoo, Annie S Chen, Aditi Raghunathan, Pang Wei Koh, Shiori Sagawa, Percy Liang, and Chelsea Finn · 2021
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Examining and combating spurious features under distribution shift
Chunting Zhou, Xuezhe Ma, Paul Michel, and Graham Neubig · 2021
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Gradient matching for domain generalization
Yuge Shi, Jeffrey Seely, Philip HS Torr, N Siddharth, Awni Hannun, Nicolas Usunier, and Gabriel Synnaeve · 2021
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Masked autoencoders are scalable vision learners
Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick · 2021
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How to train your vit? data, augmentation, and regularization in vision transformers
Andreas Steiner, Alexander Kolesnikov, Xiaohua Zhai, Ross Wightman, Jakob Uszkoreit, and Lucas Beyer · 2021
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Imagenet-21k pretraining for the masses
Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy, and Lihi Zelnik-Manor · 2021
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An empirical study of training self-supervised vision transformers
Xinlei Chen, Saining Xie, and Kaiming He · 2021
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Improving out-of-distribution robustness via selective augmentation
Huaxiu Yao, Yu Wang, Sai Li, Linjun Zhang, Weixin Liang, James Zou, and Chelsea Finn · 2022
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Cadg: A model based on cross attention for domain generalization
Chengqiu Dai, Fan Li, Xiyao Li, and Don Xie · 2022
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Asymmetry learning for counterfactually-invariant classification in OOD tasks
S Chandra Mouli and Bruno Ribeiro · 2022
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A fine-grained analysis on distribution shift
Olivia Wiles, Sven Gowal, Florian Stimberg, Sylvestre-Alvise Rebuffi, Ira Ktena, Krishnamurthy Dj Dvijotham, and Ali Taylan Cemgil · 2022
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Fine-tuning can distort pretrained features and underperform out-of-distribution
Ananya Kumar, Aditi Raghunathan, Robbie Jones, Tengyu Ma, and Percy Liang · 2022
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