125 citations · 409 across the 32 of their papers we have counts for
4 papers · 1 filter
Inexact Proximal Cubic Regularized Newton Methods for Convex Optimization
Chaobing Song, Ji Liu, Yong Jiang
In this paper, we use Proximal Cubic regularized Newton Methods (PCNM) to optimize the sum of a smooth convex function and a non-smooth convex function, where we use inexact gradie…
Stochastic Primal-Dual Method for Empirical Risk Minimization with Per-Iteration Complexity
Conghui Tan, Tong Zhang, Shiqian Ma +1
Regularized empirical risk minimization problem with linear predictor appears frequently in machine learning. In this paper, we propose a new stochastic primal-dual method to solve…
Revisit Batch Normalization: New Understanding from an Optimization View and a Refinement via Composition Optimization
Xiangru Lian, Ji Liu
Batch Normalization (BN) has been used extensively in deep learning to achieve faster training process and better resulting models. However, whether BN works strongly depends on ho…
Stochastically Controlled Stochastic Gradient for the Convex and Non-convex Composition problem
Liu Liu, Ji Liu, Cho-Jui Hsieh +1
In this paper, we consider the convex and non-convex composition problem with the structure , where $G( x )=\frac{1}{n}\sum\…