Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings. - Raef Bassily; Cristóbal Guzmán; Michael Menart (NeurIPS, 2021)

Differentially Private Generalized Linear Models Revisited. - Raman Arora; Raef Bassily; Cristóbal Guzmán; Michael Menart; Enayat Ullah (NeurIPS, 2022)

Differentially Private Algorithms for the Stochastic Saddle Point Problem with Optimal Rates for the Strong Gap. - Raef Bassily; Cristóbal Guzmán; Michael Menart (COLT, 2023)

Faster Rates of Convergence to Stationary Points in Differentially Private Optimization. - Raman Arora; Raef Bassily; Tomás González; Cristóbal Guzmán; Michael Menart; Enayat Ullah (ICML, 2023)

Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates. - Michael Menart; Enayat Ullah; Raman Arora; Raef Bassily; Cristóbal Guzmán (ALT, 2024)

Private Algorithms for Stochastic Saddle Points and Variational Inequalities: Beyond Euclidean Geometry. - Raef Bassily; Cristóbal Guzmán; Michael Menart (NeurIPS, 2024)

Public-data Assisted Private Stochastic Optimization: Power and Limitations. - Enayat Ullah; Michael Menart; Raef Bassily; Cristóbal Guzmán; Raman Arora (NeurIPS, 2024)

Private Rate-Constrained Optimization with Applications to Fair Learning. - Mohammad Yaghini; Tudor Cebere; Michael Menart; Aurélien Bellet; Nicolas Papernot (CoRR, 2025)