2 citations · 3 across the 4 of their papers we have counts for
6 papers
Fast and Scalable Cellwise-Robust Ensembles for High-Dimensional Data
Anthony Christidis, Jeyshinee Pyneeandee, Gabriela Cohen Freue
Variable selection and ensemble methods are central to high-dimensional modelling, enabling the identification of relevant predictors and the construction of stable predictive sign…
Data-Driven Logistic Regression Ensembles With Applications in Genomics
Anthony-Alexander Christidis, Stefan Van Aelst, Ruben Zamar
Advances in data collecting technologies in genomics have significantly increased the need for tools designed to study the genetic basis of many diseases. Effective statistical met…
Fast Robust Regression via Orthogonal Block Updates
Anthony Christidis, Matias Salibian-Barrera
Robust regression methods, particularly MM-estimators, are essential for analyzing datasets where heavy-tailed noise or high-leverage outliers may be present. Algorithms to compute…
Objective-Driven Ensembles: Bridging the Gap Between Interpretable Sparsity and Algorithmic Prediction
Anthony Christidis, Stefan Van Aelst, Ruben Zamar
Sparse methods (e.g., Best Subset Selection, Elastic Net) are the standard approach for obtaining interpretable models, but they can suffer from high variance and vulnerability to…
Robust Multi-Model Subset Selection
Anthony-Alexander Christidis, Gabriela Cohen-Freue
Outlying observations can be challenging to handle and adversely affect subsequent analyses, especially in data with increasing dimensional complexity. Although outliers are not al…
Multi-Model Subset Selection
Anthony-Alexander Christidis, Stefan Van Aelst, Ruben Zamar
The two primary approaches for high-dimensional regression problems are sparse methods (e.g., best subset selection, which uses the L0-norm in the penalty) and ensemble methods (e.…