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Supervising Feature Influence
Standard methods for training classifiers that minimize empirical risk do not constrain the behavior of the classifier on such datapoints.
Use Privacy in Data-Driven Systems: Theory and Experiments with Machine Learnt Programs
For a specific instantiation of this definition, we present a program analysis technique that detects instances of proxy use in a model, and provides a witness that identifies which parts of the corresponding program exhibit the behavior.
Revisiting Differentially Private Regression: Lessons From Learning Theory and their Consequences
Perhaps more importantly, our theory reveals that the most basic mechanism in differential privacy, output perturbation, can be used to obtain a better tradeoff for all convex-Lipschitz-bounded learning tasks.
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