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Found 16 papers, 1 papers with code
Slow Kill for Big Data Learning
Big-data applications often involve a vast number of observations and features, creating new challenges for variable selection and parameter estimation.
Supervised Multivariate Learning with Simultaneous Feature Auto-grouping and Dimension Reduction
In this paper, new information-theoretical limits are presented to reveal the intrinsic cost of seeking for clusters, as well as the blessing from dimensionality in multivariate learning.
Analysis of Generalized Bregman Surrogate Algorithms for Nonsmooth Nonconvex Statistical Learning
Modern statistical applications often involve minimizing an objective function that may be nonsmooth and/or nonconvex.
Gaining Outlier Resistance with Progressive Quantiles: Fast Algorithms and Theoretical Studies
Outliers widely occur in big-data applications and may severely affect statistical estimation and inference.
Network Pruning via Annealing and Direct Sparsity Control
The attractive fact that the network size keeps dropping throughout the iterations makes it suitable for the pruning of any untrained or pre-trained network.
Finding Deep Local Optima Using Network Pruning
However, in non-vision sparse datasets, especially with many irrelevant features where a standard neural network would overfit, this might not be the case and there might be many non-equivalent local optima.
On Cross-validation for Sparse Reduced Rank Regression
In high-dimensional data analysis, regularization methods pursuing sparsity and/or low rank have received a lot of attention recently.
Indirect Gaussian Graph Learning beyond Gaussianity
This paper studies how to capture dependency graph structures from real data which may not be Gaussian.
Iterative proportional scaling revisited: a modern optimization perspective
This paper revisits the classic iterative proportional scaling (IPS) from a modern optimization perspective.
Sparse Generalized Principal Component Analysis for Large-scale Applications beyond Gaussianity
Principal Component Analysis (PCA) is a dimension reduction technique.
Joint Association Graph Screening and Decomposition for Large-scale Linear Dynamical Systems
In particular, our method can pre-determine and remove unnecessary edges based on the joint graphical structure, referred to as JAG screening, and can decompose a large network into smaller subnetworks in a robust manner, referred to as JAG decomposition.
Group Regularized Estimation under Structural Hierarchy
We give the minimax lower bounds for strong and weak hierarchical variable selection and show that the proposed estimators enjoy sharp rate oracle inequalities.
Learning Topology and Dynamics of Large Recurrent Neural Networks
Large-scale recurrent networks have drawn increasing attention recently because of their capabilities in modeling a large variety of real-world phenomena and physical mechanisms.
Selective Factor Extraction in High Dimensions
This paper studies simultaneous feature selection and extraction in supervised and unsupervised learning.
Feature Selection with Annealing for Computer Vision and Big Data Learning
Many computer vision and medical imaging problems are faced with learning from large-scale datasets, with millions of observations and features.
Group Iterative Spectrum Thresholding for Super-Resolution Sparse Spectral Selection
Recently, sparsity-based algorithms are proposed for super-resolution spectrum estimation.
