39 citations · 135 across the 9 of their papers we have counts for
9 papers
Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks
Zhishuai Guo, Mingrui Liu, Zhuoning Yuan +3
In this paper, we study distributed algorithms for large-scale AUC maximization with a deep neural network as a predictive model. Although distributed learning techniques have been…
Improved Schemes for Episodic Memory-based Lifelong Learning
Yunhui Guo, Mingrui Liu, Tianbao Yang +1
Current deep neural networks can achieve remarkable performance on a single task. However, when the deep neural network is continually trained on a sequence of tasks, it seems to g…
Stochastic AUC Maximization with Deep Neural Networks
Mingrui Liu, Zhuoning Yuan, Yiming Ying +1
Stochastic AUC maximization has garnered an increasing interest due to better fit to imbalanced data classification. However, existing works are limited to stochastic AUC maximizat…
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…
Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions
Mingrui Liu, Xiaoxuan Zhang, Lijun Zhang +2
Error bound conditions (EBC) are properties that characterize the growth of an objective function when a point is moved away from the optimal set. They have recently received incre…
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…