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Found 2 papers, 1 papers with code
Scalable Optimal Classifiers for Adversarial Settings under Uncertainty
We consider the problem of finding optimal classifiers in an adversarial setting where the class-1 data is generated by an attacker whose objective is not known to the defender -- an aspect that is key to realistic applications but has so far been overlooked in the literature.
Linear Regression from Strategic Data Sources
In this paper, we study a setting in which features are public but individuals choose the precision of the outputs they reveal to an analyst.
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