1 citations · 1 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
Anytime-Valid Conformal Risk Control
Bror Hultberg, Dave Zachariah, Antônio H. Ribeiro
Prediction sets provide a means of quantifying the uncertainty in predictive tasks. Using held out calibration data, conformal prediction and risk control can produce prediction se…
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…
Learning Treatment Allocations with Risk Control Under Partial Identifiability
Sofia Ek, Dave Zachariah
Learning beneficial treatment allocations for a patient population is an important problem in precision medicine. Many treatments come with adverse side effects that are not commen…
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…