36 citations · 59 across the 6 of their papers we have counts for
6 papers
Robustness to Unbounded Smoothness of Generalized SignSGD
Michael Crawshaw, Mingrui Liu, Francesco Orabona +2
Traditional analyses in non-convex optimization typically rely on the smoothness assumption, namely requiring the gradients to be Lipschitz. However, recent evidence shows that thi…
A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks
Mingrui Liu, Zhenxun Zhuang, Yunwei Lei +1
In distributed training of deep neural networks, people usually run Stochastic Gradient Descent (SGD) or its variants on each machine and communicate with other machines periodical…
Understanding AdamW through Proximal Methods and Scale-Freeness
Zhenxun Zhuang, Mingrui Liu, Ashok Cutkosky +1
Adam has been widely adopted for training deep neural networks due to less hyperparameter tuning and remarkable performance. To improve generalization, Adam is typically used in ta…
On the Initialization for Convex-Concave Min-max Problems
Mingrui Liu, Francesco Orabona
Convex-concave min-max problems are ubiquitous in machine learning, and people usually utilize first-order methods (e.g., gradient descent ascent) to find the optimal solution. One…
On the Last Iterate Convergence of Momentum Methods
Xiaoyu Li, Mingrui Liu, Francesco Orabona
SGD with Momentum (SGDM) is a widely used family of algorithms for large-scale optimization of machine learning problems. Yet, when optimizing generic convex functions, no advantag…
Adam: A Stochastic Method with Adaptive Variance Reduction
Mingrui Liu, Wei Zhang, Francesco Orabona +1
Adam is a widely used stochastic optimization method for deep learning applications. While practitioners prefer Adam because it requires less parameter tuning, its use is problemat…