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07/15/2020
A General Family of Stochastic Proximal Gradient Methods for Deep Learning
We study the training of regularized neural networks where the regulariz...
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07/02/2020
A Revision of Neural Tangent Kernel-based Approaches for Neural Networks
Recent theoretical works based on the neural tangent kernel (NTK) have s...
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05/26/2019
Stochastic Gradient Methods with Block Diagonal Matrix Adaptation
Adaptive gradient approaches that automatically adjust the learning rate...
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10/04/2017
Multitask Learning using Task Clustering with Applications to Predictive Modeling and GWAS of Plant Varieties
Inferring predictive maps between multiple input and multiple output var...
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05/13/2017
Learning task structure via sparsity grouped multitask learning
Sparse mapping has been a key methodology in many high-dimensional scien...
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04/13/2016
Removing Clouds and Recovering Ground Observations in Satellite Image Sequences via Temporally Contiguous Robust Matrix Completion
We consider the problem of removing and replacing clouds in satellite im...
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10/28/2015
Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso
Gaussian Graphical Models (GGMs) are popular tools for studying network ...
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02/19/2014
Sparse Quantile Huber Regression for Efficient and Robust Estimation
We consider new formulations and methods for sparse quantile regression ...
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07/11/2013
