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cs.LG2025
On the Gradient Complexity of Private Optimization with Private Oracles
Michael Menart, Aleksandar Nikolov
We study the running time, in terms of first order oracle queries, of differentially private empirical/population risk minimization of Lipschitz convex losses. We first consider th…
cs.LG2025
Private Rate-Constrained Optimization with Applications to Fair Learning
Mohammad Yaghini, Tudor Cebere, Michael Menart +2
Many problems in trustworthy ML can be expressed as constraints on prediction rates across subpopulations, including group fairness constraints (demographic parity, equalized odds,…