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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
Kernel Learning with Adversarial Features: Numerical Efficiency and Adaptive Regularization
Antônio H. Ribeiro, David Vävinggren, Dave Zachariah +2
Adversarial training has emerged as a key technique to enhance model robustness against adversarial input perturbations. Many of the existing methods rely on computationally expens…