39 citations · 135 across the 9 of their papers we have counts for
5 papers · 1 filter
First-order Convergence Theory for Weakly-Convex-Weakly-Concave Min-max Problems
Mingrui Liu, Hassan Rafique, Qihang Lin +1
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 we…
Weakly-Convex Concave Min-Max Optimization: Provable Algorithms and Applications in Machine Learning
Hassan Rafique, Mingrui Liu, Qihang Lin +1
Min-max problems have broad applications in machine learning, including learning with non-decomposable loss and learning with robustness to data distribution. Convex-concave min-ma…
Stochastic Non-convex Optimization with Strong High Probability Second-order Convergence
Mingrui Liu, Tianbao Yang
In this paper, we study stochastic non-convex optimization with non-convex random functions. Recent studies on non-convex optimization revolve around establishing second-order conv…
On Noisy Negative Curvature Descent: Competing with Gradient Descent for Faster Non-convex Optimization
Mingrui Liu, Tianbao Yang
The Hessian-vector product has been utilized to find a second-order stationary solution with strong complexity guarantee (e.g., almost linear time complexity in the problem's dimen…
Adaptive Accelerated Gradient Converging Methods under Holderian Error Bound Condition
Mingrui Liu, Tianbao Yang
Recent studies have shown that proximal gradient (PG) method and accelerated gradient method (APG) with restarting can enjoy a linear convergence under a weaker condition than stro…