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Breaking Fair Binary Classification with Optimal Flipping Attacks
Minimizing risk with fairness constraints is one of the popular approaches to learning a fair classifier.
Discrete-Valued Latent Preference Matrix Estimation with Graph Side Information
Ahn et al. (2018) firstly characterized the optimal sample complexity in the presence of graph side information, but the results are limited due to strict, unrealistic assumptions made on the unknown latent preference matrix and the structure of user clusters.
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