519 citations · 2.1k across the 79 of their papers we have counts for
6 papers · 1 filter
Large-Margin Determinantal Point Processes
Boqing Gong, Wei-lun Chao, Kristen Grauman +1
Determinantal point processes (DPPs) offer a powerful approach to modeling diversity in many applications where the goal is to select a diverse subset. We study the problem of lear…
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…
Two-Stage Metric Learning
Jun Wang, Ke Sun, Fei Sha +2
In this paper, we present a novel two-stage metric learning algorithm. We first map each learning instance to a probability distribution by computing its similarities to a set of f…
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…