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Neural networks can fail when the data contains spurious correlations.
“Image Counterfactual Sensitivity Analysis for Detecting Unintended Bias”
Remi Denton et al · 1906
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“Image Counterfactual Sensitivity Analysis for Detecting Unintended Bias”
Remi Denton et al · 1906
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani and David Lopez-Paz · 1907
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Martin Arjovsky, Léon Bottou, Ishaan Gulrajani and David Lopez-Paz · 1907
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“The Nature of Statistical Learning Theory”
Vladimir Vapnik · 1999
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“The Nature of Statistical Learning Theory”
Vladimir Vapnik · 1999
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“ImageNet: A large-scale hierarchical image database”
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“ImageNet Large Scale Visual Recognition Challenge”
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“ImageNet Large Scale Visual Recognition Challenge”
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“Deep Residual Learning for Image Recognition”
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“Adam: A Method for Stochastic Optimization”
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Jieyu Zhao et al · 2017
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“Adam: A Method for Stochastic Optimization”
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Jieyu Zhao et al · 2017
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Os Keyes · 2018
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“Deep Learning Generalizes Because the Parameter-Function Map Is Biased towards Simple Functions”
Guillermo Valle-Perez, Chico. Camargo and Ard. Louis · 2018
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Adina Williams, Nikita Nangia and Samuel Bowman · 2018
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John. Zech et al · 2018
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“The Misgendering Machines: Trans/HCI Implications of Automatic Gender Recognition”
Os Keyes · 2018
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“Deep Learning Generalizes Because the Parameter-Function Map Is Biased towards Simple Functions”
Guillermo Valle-Perez, Chico. Camargo and Ard. Louis · 2018
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“A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference”
Adina Williams, Nikita Nangia and Samuel Bowman · 2018
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“Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study”
John. Zech et al · 2018
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“Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification”
Daniel Borkan et al · 2019
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“Does Object Recognition Work for Everyone?”
Terrance de Vries, Ishan Misra, Changhan Wang and Laurens van der Maaten · 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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Jeremy Irvin et al · 2019
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“SGD on Neural Networks Learns Functions of Increasing Complexity”
Dimitris Kalimeris et al · 2019
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“The Risk of Racial Bias in Hate Speech Detection”
Maarten Sap et al · 2019
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“Fairness and Abstraction in Sociotechnical Systems”
Andrew. Selbst et al · 2019
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“Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations”
Tianlu Wang et al · 2019
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“Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification”
Daniel Borkan et al · 2019
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“Does Object Recognition Work for Everyone?”
Terrance de Vries, Ishan Misra, Changhan Wang and Laurens van der Maaten · 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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“CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison”
Jeremy Irvin et al · 2019
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“SGD on Neural Networks Learns Functions of Increasing Complexity”
Dimitris Kalimeris et al · 2019
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“The Risk of Racial Bias in Hate Speech Detection”
Maarten Sap et al · 2019
“On the genealogy of machine learning datasets: A critical history of ImageNet”
Remi Denton et al · 2021
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“Adaptive Methods for Real-World Domain Generalization”
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“Reduced, Reused and Recycled: The Life of a Dataset in Machine Learning Research”
Bernard Koch, Remi Denton, Alex Hanna and Jacob Foster · 2021
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Luca Scimeca et al · 2021
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Cited alongside, same era.
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Andrew. Selbst et al · 2019
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“A Systematic Study of Bias Amplification”
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“Simplicity Bias Leads to Amplified Performance Disparities”
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“On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets”
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“Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases”
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“Does Progress On Object Recognition Benchmarks Improve Real-World Generalization?”
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“Spurious Correlations and Where to Find Them”
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“It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance”
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“Change is Hard: A Closer Look at Subpopulation Shift”
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“NICO++: Towards Better Benchmarking for Domain Generalization”
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“Simplicity Bias Leads to Amplified Performance Disparities”
Samuel Bell and Levent Sagun · 2023
Later among the works it cites.
“Exploring Why Object Recognition Performance Degrades Across Income Levels and Geographies with Factor Annotations”
Laura Gustafson et al · 2023
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“Towards Mitigating Spurious Correlations in the Wild: A Benchmark and a More Realistic Dataset”
Siddharth Joshi et al · 2023
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“On the Need for a Language Describing Distribution Shifts: Illustrations on Tabular Datasets”
Jiashuo Liu, Tianyu Wang, Peng Cui and Hongseok Namkoong · 2023
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“Spawrious: A Benchmark for Fine Control of Spurious Correlation Biases”
Aengus Lynch, Gbètondji.-S. Dovonon, Jean Kaddour and Ricardo Silva · 2023
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“Does Progress On Object Recognition Benchmarks Improve Real-World Generalization?”
Megan Richards, Polina Kirichenko, Diane Bouchacourt and Mark Ibrahim · 2023
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“Spurious Correlations and Where to Find Them”
Gautam Sreekumar and Vishnu Boddeti · 2023
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“It Takes Two to Tango: Navigating Conceptualizations of NLP Tasks and Measurements of Performance”
Arjun Subramonian, Xingdi Yuan, Hal Daumé and Su Blodgett · 2023
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“Change is Hard: A Closer Look at Subpopulation Shift”
Yuzhe Yang, Haoran Zhang, Dina Katabi and Marzyeh Ghassemi · 2023
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“NICO++: Towards Better Benchmarking for Domain Generalization”
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