196 citations · 294 across the 19 of their papers we have counts for
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cs.LG2019
Learning Fair Representations for Kernel Models
Zilong Tan, Samuel Yeom, Matt Fredrikson +1
Fair representations are a powerful tool for establishing criteria like statistical parity, proxy non-discrimination, and equality of opportunity in learned models. Existing techni…
cs.LG2019
Stolen Memories: Leveraging Model Memorization for Calibrated White-Box Membership Inference
Klas Leino, Matt Fredrikson
Membership inference (MI) attacks exploit the fact that machine learning algorithms sometimes leak information about their training data through the learned model. In this work, we…
cs.LG2019
FlipTest: Fairness Testing via Optimal Transport
Emily Black, Samuel Yeom, Matt Fredrikson
We present FlipTest, a black-box technique for uncovering discrimination in classifiers. FlipTest is motivated by the intuitive question: had an individual been of a different prot…