32 citations · 91 across the 16 of their papers we have counts for
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cs.LG2018★ 32 cited
The implicit fairness criterion of unconstrained learning
Lydia T. Liu, Max Simchowitz, Moritz Hardt
We clarify what fairness guarantees we can and cannot expect to follow from unconstrained machine learning. Specifically, we characterize when unconstrained learning on its own imp…
cs.LG2018
Delayed Impact of Fair Machine Learning
Lydia T. Liu, Sarah Dean, Esther Rolf +2
Fairness in machine learning has predominantly been studied in static classification settings without concern for how decisions change the underlying population over time. Conventi…
cs.LG2018★ 26 cited
On the Local Minima of the Empirical Risk
Chi Jin, Lydia T. Liu, Rong Ge +1
Population risk is always of primary interest in machine learning; however, learning algorithms only have access to the empirical risk. Even for applications with nonconvex nonsmoo…