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Bandit Learning in Decentralized Matching Markets
We study two-sided matching markets in which one side of the market (the...
12/14/2020 ∙ by Lydia T. Liu, et al. ∙
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Balancing Competing Objectives with Noisy Data: Score-Based Classifiers for Welfare-Aware Machine Learning
While real-world decisions involve many competing objectives, algorithmi...
03/15/2020 ∙ by Esther Rolf, et al. ∙
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The Disparate Equilibria of Algorithmic Decision Making when Individuals Invest Rationally
The long-term impact of algorithmic decision making is shaped by the dyn...
10/04/2019 ∙ by Lydia T. Liu, et al. ∙
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Competing Bandits in Matching Markets
Stable matching, a classical model for two-sided markets, has long been ...
06/12/2019 ∙ by Lydia T. Liu, et al. ∙
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Steerable ePCA
In photon-limited imaging, the pixel intensities are affected by photon ...
12/20/2018 ∙ by Zhizhen Zhao, et al. ∙
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Group calibration is a byproduct of unconstrained learning
Much recent work on fairness in machine learning has focused on how well...
08/29/2018 ∙ by Lydia T. Liu, et al. ∙
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Minimizing Nonconvex Population Risk from Rough Empirical Risk
Population risk---the expectation of the loss over the sampling mechanis...
03/25/2018 ∙ by Chi Jin, et al. ∙
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Delayed Impact of Fair Machine Learning
Fairness in machine learning has predominantly been studied in static cl...
03/12/2018 ∙ by Lydia T. Liu, et al. ∙
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