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Errors in labels obtained via human annotation adversely affect a model's performance.
The influence curve and its role in robust estimation
Frank R Hampel · 1974
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Residuals and influence in regression
R Dennis Cook and Sanford Weisberg · 1982
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Assessment of local influence
R Dennis Cook · 1986
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Use of brier score to assess binary predictions
Kaspar Rufibach · 2010
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Label-noise robust logistic regression and its applications
Jakramate Bootkrajang and Ata Kabán · 2012
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Classification in the presence of label noise: a survey
Benoît Frénay and Michel Verleysen · 2013
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Learning with noisy labels
Nagarajan Natarajan, Inderjit S Dhillon, Pradeep K Ravikumar, and Ambuj Tewari · 2013
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Training deep neural networks on noisy labels with bootstrapping
Scott Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich · 2014
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Dropout: a simple way to prevent neural networks from overfitting
Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov · 2014
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Unique in the shopping mall: On the reidentifiability of credit card metadata
Yves-Alexandre De Montjoye, Laura Radaelli, Vivek Kumar Singh, and Alex “Sandy” Pentland · 2015
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Obtaining well calibrated probabilities using bayesian binning
Mahdi Pakdaman Naeini, Gregory Cooper, and Milos Hauskrecht · 2015
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Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
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A closer look at memorization in deep networks
Devansh Arpit, Stanisław Jastrzębski, Nicolas Ballas, David Krueger, Emmanuel Bengio, Maxinder S Kanwal, Tegan Maharaj, Asja Fischer, Aaron Courville, Yoshua Bengio, et al · 2017
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Semantics derived automatically from language corpora contain human-like biases
Aylin Caliskan, Joanna J Bryson, and Arvind Narayanan · 2017
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Robust loss functions under label noise for deep neural networks
Aritra Ghosh, Himanshu Kumar, and PS Sastry · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q Weinberger · 2017
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q Weinberger · 2017
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Deep learning is robust to massive label noise
David Rolnick, Andreas Veit, Serge Belongie, and Nir Shavit · 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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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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Using trusted data to train deep networks on labels corrupted by severe noise
Dan Hendrycks, Mantas Mazeika, Duncan Wilson, and Kevin Gimpel · 2018
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Learning to reweight examples for robust deep learning
Mengye Ren, Wenyuan Zeng, Bin Yang, and Raquel Urtasun · 2018
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Representer point selection for explaining deep neural networks
Chih-Kuan Yeh, Joon Kim, Ian En-Hsu Yen, and Pradeep K Ravikumar · 2018
Cited alongside, same era.
Differential privacy has disparate impact on model accuracy
Eugene Bagdasaryan, Omid Poursaeed, and Vitaly Shmatikov · 2019
Cited alongside, same era.
Fairness and Machine Learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2019
Cited alongside, same era.
Understanding the origins of bias in word embeddings
Marc-Etienne Brunet, Colleen Alkalay-Houlihan, Ashton Anderson, and Richard Zemel · 2019
Cited alongside, same era.
Unsupervised cross-lingual representation learning at scale
Alexis Conneau, Kartikay Khandelwal, Naman Goyal, Vishrav Chaudhary, Guillaume Wenzek, Francisco Guzmán, Edouard Grave, Myle Ott, Luke Zettlemoyer, and Veselin Stoyanov · 2019
Certified robustness to label-flipping attacks via randomized smoothing
Elan Rosenfeld, Ezra Winston, Pradeep Ravikumar, and Zico Kolter · 2020
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Combating noisy labels by agreement: A joint training method with co-regularization
Hongxin Wei, Lei Feng, Xiangyu Chen, and Bo An · 2020
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Fairness on the ground: Applying algorithmic fairness approaches to production systems
Chloé Bakalar, Renata Barreto, Stevie Bergman, Miranda Bogen, Bobbie Chern, Sam Corbett-Davies, Melissa Hall, Isabel Kloumann, Michelle Lam, Joaquin Quiñonero Candela, et al · 2021
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Environment inference for invariant learning
Elliot Creager, Jörn-Henrik Jacobsen, and Richard Zemel · 2021
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Leveraging expert consistency to improve algorithmic decision support
Maria De-Arteaga, Artur Dubrawski, and Alexandra Chouldechova · 2021
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Cited alongside, same era.
What do compressed deep neural networks forget?
Sara Hooker, Aaron Courville, Gregory Clark, Yann Dauphin, and Andrea Frome · 2019
Cited alongside, same era.
Multiaccuracy: Black-box post-processing for fairness in classification
Michael P Kim, Amirata Ghorbani, and James Zou · 2019
Cited alongside, same era.
The implicit fairness criterion of unconstrained learning
Lydia T Liu, Max Simchowitz, and Moritz Hardt · 2019
Cited alongside, same era.
Actionable auditing: Investigating the impact of publicly naming biased performance results of commercial ai products
Inioluwa Deborah Raji and Joy Buolamwini · 2019
Cited alongside, same era.
Relatif: Identifying explanatory training samples via relative influence
Elnaz Barshan, Marc-Etienne Brunet, and Gintare Karolina Dziugaite · 2020
Cited alongside, same era.
Goemotions: A dataset of fine-grained emotions
Dorottya Demszky, Dana Movshovitz-Attias, Jeongwoo Ko, Alan Cowen, Gaurav Nemade, and Sujith Ravi · 2020
Cited alongside, same era.
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Retiring adult: New datasets for fair machine learning
Frances Ding, Moritz Hardt, John Miller, and Ludwig Schmidt · 2021
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Resolving training biases via influence-based data relabeling
Shuming Kong, Yanyan Shen, and Linpeng Huang · 2021
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Fairness-aware pac learning from corrupted data
Nikola Konstantinov and Christoph H Lampert · 2021
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How does a neural network’s architecture impact its robustness to noisy labels?
Jingling Li, Mozhi Zhang, Keyulu Xu, John Dickerson, and Jimmy Ba · 2021
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Pervasive label errors in test sets destabilize machine learning benchmarks
Curtis G Northcutt, Anish Athalye, and Jonas Mueller · 2021
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Interpretable data-based explanations for fairness debugging
Romila Pradhan, Jiongli Zhu, Boris Glavic, and Babak Salimi · 2021
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Fair classification with group-dependent label noise
Jialu Wang, Yang Liu, and Caleb Levy · 2021
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Logit-based uncertainty measure in classification
Huiyu Wu and Diego Klabjan · 2021
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To be robust or to be fair: Towards fairness in adversarial training
Han Xu, Xiaorui Liu, Yaxin Li, Anil Jain, and Jiliang Tang · 2021
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Understanding deep learning (still) requires rethinking generalization
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals · 2021
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30% of google’s emotions dataset is mislabeled, 2022
Edwin Chen · 2022
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Instance-dependent label-noise learning with manifold-regularized transition matrix estimation
De Cheng, Tongliang Liu, Yixiong Ning, Nannan Wang, Bo Han, Gang Niu, Xinbo Gao, and Masashi Sugiyama · 2022
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A systematic study of bias amplification
Melissa Hall, Laurens van der Maaten, Laura Gustafson, and Aaron Adcock · 2022
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Achieving fairness at no utility cost via data reweighing with influence
Peizhao Li and Hongfu Liu · 2022
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How i found nearly 300,000 errors in ms coco, 2022
Edwin Murdoch · 2022
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Prasanna Sattigeri, Soumya Ghosh, Inkit Padhi, Pierre Dognin, and Kush R Varshney · 2022
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Pruning has a disparate impact on model accuracy
Cuong Tran, Ferdinando Fioretto, Jung-Eun Kim, and Rakshit Naidu · 2022
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