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Machine learning models are trained to minimize the mean loss for a single metric, and thus typically do not consider fairness and robustness.
Methods of reducing sample size in monte carlo computations
Herman Kahn and Andy W Marshall · 1953
Earlier work this paper cites.
Constrained differential optimization
John C Platt and Alan H Barr · 1987
Earlier work this paper cites.
Gradient-based optimization of hyperparameters
Yoshua Bengio · 2000
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Learning and evaluating classifiers under sample selection bias
Bianca Zadrozny · 2004
Earlier work this paper cites.
Self-paced learning for latent variable models
M Kumar, Benjamin Packer, and Daphne Koller · 2010
Earlier work this paper cites.
Ensemble of exemplar-svms for object detection and beyond
Tomasz Malisiewicz, Abhinav Gupta, and Alexei A Efros · 2011
Earlier work this paper cites.
Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups
Geoffrey Hinton, Li Deng, Dong Yu, George E Dahl, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara N Sainath, et al · 2012
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
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Earlier work this paper cites.
Learning to learn
Sebastian Thrun and Lorien Pratt · 2012
Earlier work this paper cites.
Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
Earlier work this paper cites.
Learning to learn by gradient descent by gradient descent
Marcin Andrychowicz, Misha Denil, Sergio Gomez, Matthew W Hoffman, David Pfau, Tom Schaul, Brendan Shillingford, and Nando De Freitas · 2016
Earlier work this paper cites.
Equality of opportunity in supervised learning
Moritz Hardt, Eric Price, and Nati Srebro · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
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A guide to deep learning in healthcare
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Data parameters: A new family of parameters for learning a differentiable curriculum
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Jun Shu, Qi Xie, Lixuan Yi, Qian Zhao, Sanping Zhou, Zongben Xu, and Deyu Meng · 2019
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Metric-optimized example weights
Sen Zhao, Mahdi Milani Fard, Harikrishna Narasimhan, and Maya R. Gupta · 2019
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Identifying and correcting label bias in machine learning
Heinrich Jiang and Ofir Nachum · 2020
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Fairness without demographics through adversarially reweighted learning
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Learning to reweight examples for robust deep learning
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Optimization with non-differentiable constraints with applications to fairness, recall, churn, and other goals
Andrew Cotter, Heinrich Jiang, Maya R Gupta, Serena Wang, Taman Narayan, Seungil You, and Karthik Sridharan
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Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, and Ed H Chi · 2020
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Fair: Fair adversarial instance re-weighting
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Generalization of model-agnostic meta-learning algorithms: Recurring and unseen tasks
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Sample selection for fair and robust training
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