Private Convex Optimization via Exponential Mechanism - Differential Privacy for Machine Learning

Private Convex Optimization via Exponential Mechanism - Differential Privacy for Machine Learning

Explore private convex optimization through the exponential mechanism in this Google TechTalk presented by Daogao Liu. Delve into differential privacy for machine learning, covering topics such as noisy stochastic gradient descent and the regularized exponential mechanism. Examine isoperimetric inequality for strongly log-concave measures and concentration bounds for Lipschitz functions. Learn about DP-Stochastic Convex Optimization and its intuition. Discover new sampling algorithms and their applications in DP-ERM and DP-SCO. Gain insights into bounding generalization error, Wasserstein distance, KL divergence, and population loss. Understand the contributions and open problems in this field of private convex optimization.