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Subpopulation shift widely exists in many real-world machine learning applications, referring to the training and test distributions containing the same subpopulation groups but varying in subpopulation frequencies.
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Rademacher and gaussian complexities: Risk bounds and structural results
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Kullback-leibler divergence constrained distributionally robust optimization
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Deep learning face attributes in the wild
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Big data’s disparate impact
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Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Utilitarianism
John Stuart Mill · 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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Deep coral: Correlation alignment for deep domain adaptation
Baochen Sun and Kate Saenko · 2016
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Snapshot ensembles: Train 1, get m for free
Gao Huang, Yixuan Li, Geoff Pleiss, Zhuang Liu, John E Hopcroft, and Kilian Q Weinberger · 2017
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Uncertainty-driven imagination for continuous deep reinforcement learning
Gabriel Kalweit and Joschka Boedecker · 2017
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What uncertainties do we need in bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Focal loss for dense object detection
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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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Fairness without demographics in repeated loss minimization
Tatsunori Hashimoto, Megha Srivastava, Hongseok Namkoong, and Percy Liang · 2018
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Does distributionally robust supervised learning give robust classifiers?
Weihua Hu, Gang Niu, Issei Sato, and Masashi Sugiyama · 2018
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Multi-task learning using uncertainty to weigh losses for scene geometry and semantics
Alex Kendall, Yarin Gal, and Roberto Cipolla · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, and David Lopez-Paz · 2018
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An investigation of why overparameterization exacerbates spurious correlations
Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, and Percy Liang · 2020
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Adversarial domain adaptation with domain mixup
Minghao Xu, Jian Zhang, Bingbing Ni, Teng Li, Chengjie Wang, Qi Tian, and Wenjun Zhang · 2020
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Invariance principle meets information bottleneck for out-of-distribution generalization
Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, and Irina Rish · 2021
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Dropout: Explicit forms and capacity control
Raman Arora, Peter Bartlett, Poorya Mianjy, and Nathan Srebro · 2021
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Iterative distillation for better uncertainty estimates in multitask emotion recognition
Didan Deng, Liang Wu, and Bertram E Shi · 2021
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Uncertainty-aware multi-view representation learning
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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Nuanced metrics for measuring unintended bias with real data for text classification
Daniel Borkan, Lucas Dixon, Jeffrey Sorensen, Nithum Thain, and Lucy Vasserman · 2019
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What is the effect of importance weighting in deep learning?
Jonathon Byrd and Zachary Lipton · 2019
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Learning imbalanced datasets with label-distribution-aware margin loss
Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Arechiga, and Tengyu Ma · 2019
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Class-balanced loss based on effective number of samples
Yin Cui, Menglin Jia, Tsung-Yi Lin, Yang Song, and Serge Belongie · 2019
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
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Bias-reduced uncertainty estimation for deep neural classifiers
Yonatan Geifman, Guy Uziel, and Ran El-Yaniv · 2019
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Yu Geng, Zongbo Han, Changqing Zhang, and Qinghua Hu · 2021
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Training independent subnetworks for robust prediction
Marton Havasi, Rodolphe Jenatton, Stanislav Fort, Jeremiah Zhe Liu, Jasper Snoek, Balaji Lakshminarayanan, Andrew Mingbo Dai, and Dustin Tran · 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, et al · 2021
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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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Mural: Meta-learning uncertainty-aware rewards for outcome-driven reinforcement learning
Kevin Li, Abhishek Gupta, Ashwin Reddy, Vitchyr H Pong, Aurick Zhou, Justin Yu, and Sergey Levine · 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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Trustworthy multimodal regression with mixture of normal-inverse gamma distributions
Huan Ma, Zongbo Han, Changqing Zhang, Huazhu Fu, Joey Tianyi Zhou, and Qinghua Hu · 2021
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Modeling the second player in distributionally robust optimization
Paul Michel, Tatsunori Hashimoto, and Graham Neubig · 2021
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Neural Network Intelligence, 1 2021
Microsoft · 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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Understanding the role of importance weighting for deep learning
Da Xu, Yuting Ye, and Chuanwei Ruan · 2021
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Doro: Distributional and outlier robust optimization
Runtian Zhai, Chen Dan, Zico Kolter, and Pradeep Ravikumar · 2021
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Boosted cvar classification
Runtian Zhai, Chen Dan, Arun Suggala, J Zico Kolter, and Pradeep Ravikumar · 2021
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How does mixup help with robustness and generalization?
Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, and James Zou · 2021
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Invariance principle meets out-of-distribution generalization on graphs
Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, Kaili Ma, Binghui Xie, Tongliang Liu, Bo Han, and James Cheng · 2022
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Trusted multi-view classification with dynamic evidential fusion
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Distributionally robust models with parametric likelihood ratios
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Focus on the common good: Group distributional robustness follows
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Improving out-of-distribution robustness via selective augmentation
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Understanding why generalized reweighting does not improve over erm
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