16 citations · 28 across the 3 of their papers we have counts for
3 papers
stat.ML2016★ 9 cited
Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data
Makoto Yamada, Jiliang Tang, Jose Lugo-Martinez +10
Machine learning methods are used to discover complex nonlinear relationships in biological and medical data. However, sophisticated learning models are computationally unfeasible…
stat.ML2014★ 3 cited
NLARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data
Makoto Yamada, Avishek Saha, Hua Ouyang +2
We propose a feature selection method that finds non-redundant features from a large and high-dimensional data in nonlinear way. Specifically, we propose a nonlinear extension of t…
cs.LG2012★ 16 cited
Stochastic Smoothing for Nonsmooth Minimizations: Accelerating SGD by Exploiting Structure
Hua Ouyang, Alexander Gray
In this work we consider the stochastic minimization of nonsmooth convex loss functions, a central problem in machine learning. We propose a novel algorithm called Accelerated Nons…