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Model-Agnostic Linear Competitors -- When Interpretable Models Compete and Collaborate with Black-Box Models
Driven by an increasing need for model interpretability, interpretable models have become strong competitors for black-box models in many real applications.
First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems
In this paper, we consider first-order convergence theory and algorithms for solving a class of non-convex non-concave min-max saddle-point problems, whose objective function is weakly convex in the variables of minimization and weakly concave in the variables of maximization.
Weakly-Convex Concave Min-Max Optimization: Provable Algorithms and Applications in Machine Learning
Min-max problems have broad applications in machine learning, including learning with non-decomposable loss and learning with robustness to data distribution.
