2020
Quadratically Regularized Subgradient Methods for Weakly Convex Optimization with Weakly Convex Constraints
ICML 2020poster
Optimization models with non-convex constraints arise in many tasks in machine learning, e.g., learning with fairness constraints or Neyman-Pearson classification with non-convex loss. Although many efficient methods have been developed with theoretical convergence guarantees for non-convex unconstr…