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In prediction tasks, there exist features that are related to the label in the same way across different settings for that task; these are semantic features or semantics.
A theory of learning from different domains
Shai Ben-David, John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer Wortman Vaughan · 2010
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Causal inference by using invariant prediction: identification and confidence intervals
Jonas Peters, Peter Bühlmann, and Nicolai Meinshausen · 2016
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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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Recognition in terra incognita
Sara Beery, Grant Van Horn, and Pietro Perona · 2018
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Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: a cross-sectional study
John R Zech, Marcus A Badgeley, Manway Liu, Anthony B Costa, Joseph J Titano, and Eric Karl Oermann · 2018
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A broad-coverage challenge corpus for sentence understanding through inference
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani, and David Lopez-Paz · 2019
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End-to-end bias mitigation by modelling biases in corpora
Rabeeh Karimi Mahabadi, Yonatan Belinkov, and James Henderson · 2019
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Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 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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Unlearn dataset bias in natural language inference by fitting the residual
He He, Sheng Zha, and Haohan Wang · 2019
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Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference
R Thomas McCoy, Ellie Pavlick, and Tal Linzen · 2019
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Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison
Jeremy Irvin, Pranav Rajpurkar, Michael Ko, Yifan Yu, Silviana Ciurea-Ilcus, Chris Chute, Henrik Marklund, Behzad Haghgoo, Robyn Ball, Katie Shpanskaya, et al · 2019
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Mimic-cxr-jpg, a large publicly available database of labeled chest radiographs
Alistair EW Johnson, Tom J Pollard, Nathaniel R Greenbaum, Matthew P Lungren, Chih-ying Deng, Yifan Peng, Zhiyong Lu, Roger G Mark, Seth J Berkowitz, and Steven Horng · 2019
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Adversarial nli: A new benchmark for natural language understanding
Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela · 2019
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Learning the difference that makes a difference with counterfactually-augmented data
Divyansh Kaushik, Eduard Hovy, and Zachary C Lipton · 2019
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Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases
Christopher Clark, Mark Yatskar, and Luke Zettlemoyer · 2019
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Repair: Removing representation bias by dataset resampling
Yi Li and Nuno Vasconcelos · 2019
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Domain generalization by solving jigsaw puzzles
Fabio M Carlucci, Antonio D’Innocente, Silvia Bucci, Barbara Caputo, and Tatiana Tommasi · 2019
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Shortcut learning in deep neural networks, 2020
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A. Wichmann · 2020
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Learning from failure: De-biasing classifier from biased classifier
Junhyun Nam, Hyuntak Cha, Sungsoo Ahn, Jaeho Lee, and Jinwoo Shin · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Learning de-biased representations with biased representations
Hyojin Bahng, Sanghyuk Chun, Sangdoo Yun, Jaegul Choo, and Seong Joon Oh · 2020
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Adversarial filters of dataset biases
Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew E. Peters, Ashish Sabharwal, and Yejin Choi · 2020
Abhishek Sinha, Kumar Ayush, Jiaming Song, Burak Uzkent, Hongxia Jin, and Stefano Ermon · 2021
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Robustness to spurious correlations in text classification via automatically generated counterfactuals
Zhao Wang and Aron Culotta · 2021
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Causally-motivated shortcut removal using auxiliary labels
Maggie Makar, Ben Packer, Dan Moldovan, Davis Blalock, Yoni Halpern, and Alexander D’Amour · 2022
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Out-of-distribution generalization in the presence of nuisance-induced spurious correlations
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Improving out-of-distribution robustness via selective augmentation
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Learning what makes a difference from counterfactual examples and gradient supervision
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Explaining the efficacy of counterfactually augmented data
Divyansh Kaushik, Amrith Setlur, Eduard Hovy, and Zachary C Lipton · 2020
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Identifying spurious correlations for robust text classification
Zhao Wang and Aron Culotta · 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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An investigation of why overparameterization exacerbates spurious correlations
Shiori Sagawa, Aditi Raghunathan, Pang Wei Koh, and Percy Liang · 2020
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Counterfactual invariance to spurious correlations: Why and how to pass stress tests
Victor Veitch, Alexander D’Amour, Steve Yadlowsky, and Jacob Eisenstein · 2021
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Umix: Improving importance weighting for subpopulation shift via uncertainty-aware mixup
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Causal inference in natural language processing: Estimation, prediction, interpretation and beyond
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Informativeness and invariance: Two perspectives on spurious correlations in natural language
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Malign overfitting: Interpolation and invariance are fundamentally at odds
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Rich feature construction for the optimization-generalization dilemma
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Last layer re-training is sufficient for robustness to spurious correlations
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Simple data balancing achieves competitive worst-group-accuracy
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Spurious feature diversification improves out-of-distribution generalization
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Understanding and improving feature learning for out-of-distribution generalization
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