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05/02/2023
Slow Kill for Big Data Learning
Big-data applications often involve a vast number of observations and fe...
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12/17/2021
Supervised Multivariate Learning with Simultaneous Feature Auto-grouping and Dimension Reduction
Modern high-dimensional methods often adopt the "bet on sparsity" princi...
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12/16/2021
Analysis of Generalized Bregman Surrogate Algorithms for Nonsmooth Nonconvex Statistical Learning
Modern statistical applications often involve minimizing an objective fu...
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12/15/2021
On Generalization and Computation of Tukey's Depth: Part II
This paper studies how to generalize Tukey's depth to problems defined i...
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12/15/2021
On Generalization and Computation of Tukey's Depth: Part I
Tukey's depth offers a powerful tool for nonparametric inference and est...
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12/15/2021
Gaining Outlier Resistance with Progressive Quantiles: Fast Algorithms and Theoretical Studies
Outliers widely occur in big-data applications and may severely affect s...
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02/11/2020
Training Efficient Network Architecture and Weights via Direct Sparsity Control
Artificial neural networks (ANNs) especially deep convolutional networks...
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12/30/2018
On Cross-validation for Sparse Reduced Rank Regression
In high-dimensional data analysis, regularization methods pursuing spars...
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10/08/2016
Indirect Gaussian Graph Learning beyond Gaussianity
This paper studies how to capture dependency graph structures from real ...
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10/08/2016
Iterative proportional scaling revisited: a modern optimization perspective
This paper revisits the classic iterative proportional scaling (IPS) fro...
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12/12/2015
Sparse Generalized Principal Component Analysis for Large-scale Applications beyond Gaussianity
Principal Component Analysis (PCA) is a dimension reduction technique. I...
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11/17/2014
Group Regularized Estimation under Structural Hierarchy
Variable selection for models including interactions between explanatory...
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11/17/2014
Joint Association Graph Screening and Decomposition for Large-scale Linear Dynamical Systems
This paper studies large-scale dynamical networks where the current stat...
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10/05/2014
Learning Topology and Dynamics of Large Recurrent Neural Networks
Large-scale recurrent networks have drawn increasing attention recently ...
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03/25/2014
Selective Factor Extraction in High Dimensions
This paper studies simultaneous feature selection and extraction in supe...
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10/10/2013
Feature Selection with Annealing for Computer Vision and Big Data Learning
Many computer vision and medical imaging problems are faced with learnin...
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07/28/2012
Group Iterative Spectrum Thresholding for Super-Resolution Sparse Spectral Selection
Recently, sparsity-based algorithms are proposed for super-resolution sp...
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03/15/2012
Approximating Higher-Order Distances Using Random Projections
We provide a simple method and relevant theoretical analysis for efficie...
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10/17/2011
Joint variable and rank selection for parsimonious estimation of high-dimensional matrices
We propose dimension reduction methods for sparse, high-dimensional mult...
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11/29/2009