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Found 6 papers, 1 papers with code
A Revision of Neural Tangent Kernel-based Approaches for Neural Networks
(2) A generalization error bound invariant of network size was derived by using a data-dependent complexity measure (CMD).
Multitask Learning using Task Clustering with Applications to Predictive Modeling and GWAS of Plant Varieties
Inferring predictive maps between multiple input and multiple output variables or tasks has innumerable applications in data science.
Sparse + Group-Sparse Dirty Models: Statistical Guarantees without Unreasonable Conditions and a Case for Non-Convexity
Imposing sparse + group-sparse superposition structures in high-dimensional parameter estimation is known to provide flexible regularization that is more realistic for many real-world problems.
Learning task structure via sparsity grouped multitask learning
We further demonstrate that our proposed method recovers groups and the sparsity patterns in the task parameters accurately by extensive experiments.
Robust Gaussian Graphical Modeling with the Trimmed Graphical Lasso
In this paper, we propose the Trimmed Graphical Lasso for robust estimation of sparse GGMs.
Minimum Distance Estimation for Robust High-Dimensional Regression
We propose a minimum distance estimation method for robust regression in sparse high-dimensional settings.
