1 citations · 1 across the 3 of their papers we have counts for
4 papers
Distributionally robust linear regression through the lens of adversarial training
Elis Stefansson, David Vävinggren, Antônio H. Ribeiro
Distributionally robust optimization (DRO) studies parameter estimation under uncertainty in the underlying probability distribution and has emerged as a principled framework for a…
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…
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…
Regularization properties of adversarially-trained linear regression
Antônio H. Ribeiro, Dave Zachariah, Francis Bach +1
State-of-the-art machine learning models can be vulnerable to very small input perturbations that are adversarially constructed. Adversarial training is an effective approach to de…