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Loss minimization is a dominant paradigm in machine learning, where a predictor is trained to minimize some loss function that depends on an uncertain event (e.g., "will it rain tomorrow?'').
Objective probability forecasts
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Convex Optimization
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Convexity, classification, and risk bounds
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Inherent trade-offs in the fair determination of risk scores
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Multicalibration: Calibration for the (computationally-identifiable) masses
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Tracking and improving information in the service of fairness
Sumegha Garg, Michael P. Kim, and Omer Reingold · 2019
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Michael P. Kim, Amirata Ghorbani, and James Zou · 2019
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Advancing Subgroup Fairness via Sleeping Experts
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