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Communication Lower Bounds for Distributed Convex Optimization: Partition Data on Features
Zihao Chen, Luo Luo, Zhihua Zhang
Recently, there has been an increasing interest in designing distributed convex optimization algorithms under the setting where the data matrix is partitioned on features. Algorith…
Tighter bound of Sketched Generalized Matrix Approximation
Haishan Ye, Qiaoming Ye, Zhihua Zhang
Generalized matrix approximation plays a fundamental role in many machine learning problems, such as CUR decomposition, kernel approximation, and matrix low rank approximation. Esp…
Revisiting Sub-sampled Newton Methods
Haishan Ye, Luo Luo, Zhihua Zhang
Many machine learning models depend on solving a large scale optimization problem. Recently, sub-sampled Newton methods have emerged to attract much attention for optimization due…
An Efficient Character-Level Neural Machine Translation
Shenjian Zhao, Zhihua Zhang
Neural machine translation aims at building a single large neural network that can be trained to maximize translation performance. The encoder-decoder architecture with an attentio…
A Proximal Stochastic Quasi-Newton Algorithm
Luo Luo, Zihao Chen, Zhihua Zhang +1
In this paper, we discuss the problem of minimizing the sum of two convex functions: a smooth function plus a non-smooth function. Further, the smooth part can be expressed by the…