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Found 4 papers, 3 papers with code
Contextual Directed Acyclic Graphs
Estimating the structure of directed acyclic graphs (DAGs) from observational data remains a significant challenge in machine learning.
The Contextual Lasso: Sparse Linear Models via Deep Neural Networks
With this capability gap in mind, we study a not-uncommon situation where the input features dichotomize into two groups: explanatory features, which are candidates for inclusion as variables in an interpretable model, and contextual features, which select from the candidate variables and determine their effects.
Flexible global forecast combinations
To date, research on economic forecasting has concentrated on local combination methods, which handle separate but related forecasting tasks in isolation.
Group selection and shrinkage: Structured sparsity for semiparametric additive models
Sparse regression and classification estimators that respect group structures have application to an assortment of statistical and machine learning problems, from multitask learning to sparse additive modeling to hierarchical selection.
