18 citations · 42 across the 16 of their papers we have counts for
11 papers · 1 filter
Calibeating for general proper losses: A Bregman divergence approach
Maximilian Fichtl, Cristóbal Guzmán, Nishant A. Mehta
This work introduces a general framework for calibeating based on regret minimization. As compared to Foster and Hart's seminal calibeating work which had specialized treatments of…
Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates
Michael Menart, Enayat Ullah, Raman Arora +2
We study private empirical risk minimization (ERM) problem for losses satisfying the -Kurdyka-Łojasiewicz (KL) condition. The Polyak-Łojasiewicz (PL) condition is a special…
Differentially Private Algorithms for the Stochastic Saddle Point Problem with Optimal Rates for the Strong Gap
Raef Bassily, Cristóbal Guzmán, Michael Menart
We show that convex-concave Lipschitz stochastic saddle point problems (also known as stochastic minimax optimization) can be solved under the constraint of -differential pr…
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…
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…
Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings
Raef Bassily, Cristóbal Guzmán, Michael Menart
We study differentially private stochastic optimization in convex and non-convex settings. For the convex case, we focus on the family of non-smooth generalized linear losses (GLLs…
Best-Case Lower Bounds in Online Learning
Cristóbal Guzmán, Nishant A. Mehta, Ali Mortazavi
Much of the work in online learning focuses on the study of sublinear upper bounds on the regret. In this work, we initiate the study of best-case lower bounds in online convex opt…
Non-Euclidean Differentially Private Stochastic Convex Optimization: Optimal Rates in Linear Time
Raef Bassily, Cristóbal Guzmán, Anupama Nandi
Differentially private (DP) stochastic convex optimization (SCO) is a fundamental problem, where the goal is to approximately minimize the population risk with respect to a convex…
Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses
Raef Bassily, Vitaly Feldman, Cristóbal Guzmán +1
Uniform stability is a notion of algorithmic stability that bounds the worst case change in the model output by the algorithm when a single data point in the dataset is replaced. A…