2 papers
stat.ML2026
A Robust Optimization Approach to Sparse Principal Component Analysis
David Vävinggren, Francis Bach, André M. H. Teixeira +2
While principal component analysis (PCA) is a fundamental tool for dimensionality reduction, its dense representations make it ill-suited for high-dimensional data. Existing method…
stat.ML2025
Efficient Optimization Algorithms for Linear Adversarial Training
Antônio H. RIbeiro, Thomas B. Schön, Dave Zahariah +1
Adversarial training can be used to learn models that are robust against perturbations. For linear models, it can be formulated as a convex optimization problem. Compared to method…