18 citations · 42 across the 16 of their papers we have counts for
5 papers · 1 filter
Optimal Algorithms for Stochastic Complementary Composite Minimization
Alexandre d'Aspremont, Cristóbal Guzmán, Clément Lezane
Inspired by regularization techniques in statistics and machine learning, we study complementary composite minimization in the stochastic setting. This problem corresponds to the m…
Faster Rates of Convergence to Stationary Points in Differentially Private Optimization
Raman Arora, Raef Bassily, Tomás González +3
We study the problem of approximating stationary points of Lipschitz and smooth functions under -differential privacy (DP) in both the finite-sum and stochastic se…
Differentially Private Generalized Linear Models Revisited
Raman Arora, Raef Bassily, Cristóbal Guzmán +2
We study the problem of -differentially private learning of linear predictors with convex losses. We provide results for two subclasses of loss functions. The first case is…
Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusions
Xufeng Cai, Chaobing Song, Cristóbal Guzmán +1
We study stochastic monotone inclusion problems, which widely appear in machine learning applications, including robust regression and adversarial learning. We propose novel varian…
Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness
Sarah Sachs, Hédi Hadiji, Tim van Erven +1
Stochastic and adversarial data are two widely studied settings in online learning. But many optimization tasks are neither i.i.d. nor fully adversarial, which makes it of fundamen…