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Outlier detection in regression: conic quadratic formulations
Andrés Gómez, José Neto
In many applications, when building linear regression models, it is important to account for the presence of outliers, i.e., corrupted input data points. Such problems can be formu…
math.OC2023
Efficient Cross-Validation for Sparse Linear Regression
Ryan Cory-Wright, Andrés Gómez
Given a high-dimensional covariate matrix and a response vector, ridge-regularized sparse linear regression selects a subset of features that explains the relationship between cova…