791 citations · 1.5k across the 57 of their papers we have counts for
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Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems
Quanming Yao, James T. Kwok, Fei Gao +2
The proximal gradient algorithm has been popularly used for convex optimization. Recently, it has also been extended for nonconvex problems, and the current state-of-the-art is the…
Loss-aware Binarization of Deep Networks
Lu Hou, Quanming Yao, James T. Kwok
Deep neural network models, though very powerful and highly successful, are computationally expensive in terms of space and time. Recently, there have been a number of attempts on…
Fast Learning with Nonconvex L1-2 Regularization
Quanming Yao, James T. Kwok, Xiawei Guo
Convex regularizers are often used for sparse learning. They are easy to optimize, but can lead to inferior prediction performance. The difference of and ($\ell_{…
Learning of Generalized Low-Rank Models: A Greedy Approach
Quanming Yao, James T. Kwok
Learning of low-rank matrices is fundamental to many machine learning applications. A state-of-the-art algorithm is the rank-one matrix pursuit (R1MP). However, it can only be used…
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity
Quanming Yao, James. T Kwok
The use of convex regularizers allows for easy optimization, though they often produce biased estimation and inferior prediction performance. Recently, nonconvex regularizers have…
Stochastic Variance-Reduced ADMM
Shuai Zheng, James T. Kwok
The alternating direction method of multipliers (ADMM) is a powerful optimization solver in machine learning. Recently, stochastic ADMM has been integrated with variance reduction…
Fast Nonsmooth Regularized Risk Minimization with Continuation
Shuai Zheng, Ruiliang Zhang, James T. Kwok
In regularized risk minimization, the associated optimization problem becomes particularly difficult when both the loss and regularizer are nonsmooth. Existing approaches either ha…