A Unified Dynamic Approach to Sparse Model Selection

no code implementations • 8 Oct 2018 • Chendi Huang, Yuan YAO

Sparse model selection is ubiquitous from linear regression to graphical models where regularization paths, as a family of estimators upon the regularization parameter varying, are computed when the regularization parameter is unknown or decided data-adaptively.

Model Selection regression

Exploring Outliers in Crowdsourced Ranking for QoE

no code implementations • 18 Jul 2017 • Qianqian Xu, Ming Yan, Chendi Huang, Jiechao Xiong, Qingming Huang, Yuan YAO

Outlier detection is a crucial part of robust evaluation for crowdsourceable assessment of Quality of Experience (QoE) and has attracted much attention in recent years.

Outlier Detection

Boosting with Structural Sparsity: A Differential Inclusion Approach

no code implementations • 16 Apr 2017 • Chendi Huang, Xinwei Sun, Jiechao Xiong, Yuan YAO

Boosting as gradient descent algorithms is one popular method in machine learning.

Image Denoising Model Selection

Split LBI: An Iterative Regularization Path with Structural Sparsity

no code implementations • NeurIPS 2016 • Chendi Huang, Xinwei Sun, Jiechao Xiong, Yuan YAO

An iterative regularization path with structural sparsity is proposed in this paper based on variable splitting and the Linearized Bregman Iteration, hence called \emph{Split LBI}.

Image Denoising Model Selection