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Presence of bias (in datasets or tasks) is inarguably one of the most critical challenges in machine learning applications that has alluded to pivotal debates in recent years.
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Unsupervised learning of invariant feature hierarchies with applications to object recognition
Marc’aurelio Ranzato, Fu Jie Huang, Y-Lan Boureau, and Yann LeCun · 2007
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Measuring and testing dependence by correlation of distances
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Visualizing data using t-SNE
Laurens van der Maaten and Geoffrey Hinton · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Econometric Analysis of Cross Section and Panel Data (2nd ed.)
Jeffrey M. Wooldridge · 2010
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Biased representation learning for domain adaptation
Fei Huang and Alexander Yates · 2012
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Undoing the damage of dataset bias
Aditya Khosla, Tinghui Zhou, Tomasz Malisiewicz, Alexei A Efros, and Antonio Torralba · 2012
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How to control confounding effects by statistical analysis
Mohamad Amin Pourhoseingholi, Ahmad Reza Baghestani, and Mohsen Vahedi · 2012
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Learning fair representations
Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman · 2014
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Deep residual learning for image recognition
K He, X Zhang, S Ren, and J Sun · 2015
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Algorithms and bias
Clair Miller · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
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Unsupervised and semi-supervised learning with categorical generative adversarial networks
Jost Tobias Springenberg · 2015
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Big data’s disparate impact
Solon Barocas and Andrew D Selbst · 2016
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L Elisa Celis, Amit Deshpande, Tarun Kathuria, and Nisheeth K Vishnoi · 2016
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Censoring representations with an adversary
Harrison Edwards and Amos Storkey · 2016
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Domain-adversarial training of neural networks
Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan, Pascal Germain, Hugo Larochelle, Fran · 2016
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Fairness in machine learning
Solon Barocas, Moritz Hardt, and Arvind Narayanan · 2017
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Learning fair classifiers: A regularization-inspired approach
Yahav Bechavod and Katrina Ligett · 2017
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Data decisions and theoretical implications when adversarially learning fair representations
Alex Beutel, Jilin Chen, Zhe Zhao, and Ed H Chi · 2017
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Unsupervised pixel-level domain adaptation with generative adversarial networks
Konstantinos Bousmalis, Nathan Silberman, David Dohan, Dumitru Erhan, and Dilip Krishnan · 2017
Cited alongside, same era.
Invariant representations without adversarial training
Daniel Moyer, Shuyang Gao, Rob Brekelmans, Aram Galstyan, and Greg Ver Steeg · 2018
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Mitigating unwanted biases with adversarial learning
Brian Hu Zhang, Blake Lemoine, and Margaret Mitchell · 2018
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Adversarial invariant feature learning with accuracy constraint for domain generalization
Kei Akuzawa, Yusuke Iwasawa, and Yutaka Matsuo · 2019
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Adversarially learned representations for information obfuscation and inference
Martin Bertran, Natalia Martinez, Afroditi Papadaki, Qiang Qiu, Miguel Rodrigues, Galen Reeves, and Guillermo Sapiro · 2019
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Why can’t i dance in the mall? learning to mitigate scene bias in action recognition
Jinwoo Choi, Chen Gao, Joseph CE Messou, and Jia-Bin Huang · 2019
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Increased brain-predicted aging in treated HIV disease
James H Cole, Jonathan Underwood, Matthan WA Caan, Davide De Francesco, Rosan A van Zoest, Robert Leech, Ferdinand WNM Wit, Peter Portegies, Gert J Geurtsen, Ben A Schmand, et al · 2017
Cited alongside, same era.
Cycada: Cycle-consistent adversarial domain adaptation
Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei A Efros, and Trevor Darrell · 2017
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Fully-adaptive feature sharing in multi-task networks with applications in person attribute classification
Yongxi Lu, Abhishek Kumar, Shuangfei Zhai, Yu Cheng, Tara Javidi, and Rogerio Feris · 2017
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Predictive modelling using neuroimaging data in the presence of confounds
Anil Rao et al · 2017
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A deeper look at dataset bias
Tatiana Tommasi, Novi Patricia, Barbara Caputo, and Tinne Tuytelaars · 2017
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Adversarial discriminative domain adaptation
Eric Tzeng, Judy Hoffman, Kate Saenko, and Trevor Darrell · 2017
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Controllable invariance through adversarial feature learning
Qizhe Xie, Zihang Dai, Yulun Du, Eduard Hovy, and Graham Neubig · 2017
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Flexibly fair representation learning by disentanglement
Elliot Creager, David Madras, Joern-Henrik Jacobsen, Marissa Weis, Kevin Swersky, Toniann Pitassi, and Richard Zemel · 2019
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Age discrimination in healthcare institutions perceived by seniors and students
Beata Dobrowolska, Bernadeta J · 2019
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A.I. could worsen health disparities
Dhruv Khullar · 2019
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Learning not to learn: Training deep neural networks with biased data
Byungju Kim, Hyunwoo Kim, Kyungsu Kim, Sungjin Kim, and Junmo Kim · 2019
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Algorithmic bias detection and mitigation: Best practices and policies to reduce consumer harms
Nicol Turner Lee, Paul Resnick, and Genie Barton · 2019
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REPAIR: Removing representation bias by dataset resampling
Yi Li and Nuno Vasconcelos · 2019
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Mitigating information leakage in image representations: A maximum entropy approach
Proteek Chandan Roy and Vishnu Naresh Boddeti · 2019
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On the global optima of kernelized adversarial representation learning
Bashir Sadeghi, Runyi Yu, and Vishnu Boddeti · 2019
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Interventional fairness: Causal database repair for algorithmic fairness
Babak Salimi, Luke Rodriguez, Bill Howe, and Dan Suciu · 2019
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Learning controllable fair representations
Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, and Stefano Ermon · 2019
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Balanced datasets are not enough: Estimating and mitigating gender bias in deep image representations
Tianlu Wang, Jieyu Zhao, Mark Yatskar, Kai-Wei Chang, and Vicente Ordonez · 2019
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Mimetics: Towards understanding human actions out of context
Philippe Weinzaepfel and Grégory Rogez · 2019
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Towards fairer datasets: Filtering and balancing the distribution of the people subtree in the imagenet hierarchy
Kaiyu Yang, Klint Qinami, Li Fei-Fei, Jia Deng, and Olga Russakovsky · 2019
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Algorithmic bias in recidivism prediction: A causal perspective
Aria Khademi and Vasant Honavar · 2020
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Training confounder-free deep learning models for medical applications
Qingyu Zhao*, Ehsan Adeli*, and Kilian M Pohl · 2020
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