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A wide range of machine learning applications such as privacy-preserving learning, algorithmic fairness, and domain adaptation/generalization among others, involve learning invariant representations of the data that aim to achieve two competing goals: (a) maximize information or accuracy with respect to a target response, and (b) maximize invariance or independence with respect to a set of protected features (e.g., for fairness, privacy, etc).
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Han Zhao, Remi Tachet des Combes, Kun Zhang, and Geoffrey J Gordon · 1901
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Han Zhao, Amanda Coston, Tameem Adel, and Geoffrey J Gordon · 1910
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Shai Ben-David, John Blitzer, Koby Crammer, and Fernando Pereira · 2007
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Stéphane Mallat · 2012
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Krikamol Muandet, David Balduzzi, and Bernhard Schölkopf · 2013
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Rich Zemel, Yu Wu, Kevin Swersky, Toni Pitassi, and Cynthia Dwork · 2013
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Brian C Ross · 2014
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Harrison Edwards and Amos Storkey · 2015
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Preserving privacy of continuous high-dimensional data with minimax filters
Jihun Hamm · 2015
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Deep learning and the information bottleneck principle
Naftali Tishby and Noga Zaslavsky · 2015
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Domain-adversarial training of neural networks
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Fredrik Johansson, Uri Shalit, and David Sontag · 2016
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Minimax rates of entropy estimation on large alphabets via best polynomial approximation
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Estimating mutual information for discrete-continuous mixtures
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Minimax filter: learning to preserve privacy from inference attacks
Jihun Hamm · 2017
Generalizing to unseen domains via distribution matching
Isabela Albuquerque, João Monteiro, Mohammad Darvishi, Tiago H Falk, and Ioannis Mitliagkas · 2019
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An information-theoretic perspective on the relationship between fairness and accuracy
Sanghamitra Dutta, Dennis Wei, Hazar Yueksel, Pin-Yu Chen, Sijia Liu, and Kush R Varshney · 2019
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Costs and benefits of fair representation learning
Daniel McNamara, Cheng Soon Ong, and Robert C Williamson · 2019
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Adversarial learning of privacy-preserving and task-oriented representations
Taihong Xiao, Yi-Hsuan Tsai, Kihyuk Sohn, Manmohan Chandraker, and Ming-Hsuan Yang · 2019
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Inherent tradeoffs in learning fair representations
Han Zhao and Geoff Gordon · 2019
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Google’s multilingual neural machine translation system: Enabling zero-shot translation
Melvin Johnson, Mike Schuster, Quoc V Le, Maxim Krikun, Yonghui Wu, Zhifeng Chen, Nikhil Thorat, Fernanda Viégas, Martin Wattenberg, Greg Corrado, et al · 2017
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Estimating individual treatment effect: generalization bounds and algorithms
Uri Shalit, Fredrik D Johansson, and David Sontag · 2017
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Privacy-preserving neural representations of text
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Deep domain generalization via conditional invariant adversarial networks
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Learning adversarially fair and transferable representations
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The cost of fairness in binary classification
Aditya Krishna Menon and Robert C Williamson · 2018
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A simple framework for contrastive learning of visual representations
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Momentum contrast for unsupervised visual representation learning
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On learning language-invariant representations for universal machine translation
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Understanding and mitigating accuracy disparity in regression
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Domain invariant representation learning with domain density transformations
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Algorithms and theory for supervised gradual domain adaptation
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