Hua Ouyang

activity

20122016

most citedStochastic Smoothing for Nonsmooth Minimizations: Accelerating SGD by Exploiting Structure

16 citations · 28 across the 3 of their papers we have counts for

collaborators

Showing stat.MLShow all

2 papers · 1 filter

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

N $^{3}$ LARS: 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…

Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data

N3LARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data

N $^{3}$ LARS: Minimum Redundancy Maximum Relevance Feature Selection for Large and High-dimensional Data