523 citations · 1.4k across the 79 of their papers we have counts for
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How to Scale Up Kernel Methods to Be As Good As Deep Neural Nets
Zhiyun Lu, Avner May, Kuan Liu +8
The computational complexity of kernel methods has often been a major barrier for applying them to large-scale learning problems. We argue that this barrier can be effectively over…
Similarity Learning for High-Dimensional Sparse Data
Kuan Liu, Aurélien Bellet, Fei Sha
A good measure of similarity between data points is crucial to many tasks in machine learning. Similarity and metric learning methods learn such measures automatically from data, b…
Sparse Compositional Metric Learning
Yuan Shi, Aurélien Bellet, Fei Sha
We propose a new approach for metric learning by framing it as learning a sparse combination of locally discriminative metrics that are inexpensive to generate from the training da…
A Distributed Frank-Wolfe Algorithm for Communication-Efficient Sparse Learning
Aurélien Bellet, Yingyu Liang, Alireza Bagheri Garakani +2
Learning sparse combinations is a frequent theme in machine learning. In this paper, we study its associated optimization problem in the distributed setting where the elements to b…