1 citations · 1 across the 8 of their papers we have counts for
4 papers · 1 filter
Adversarial Training of Linear Models under Stealthy Attacks
Lovisa Eriksson, Dave Zachariah, André M. H. Teixeira
Predictive models are widely used in many fields, but are vulnerable to false data injection attacks. To address this, detection schemes and adversarial training have been proposed…
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…
Learning plug-in surrogate endpoints for randomized experiments
Alessandro-Umberto Margueritte, Ahmet Zahid Balcıoğlu, Jesse Krijthe +2
Surrogate endpoints are used in place of long-term outcomes in randomized experiments when observing the real outcome for a large enough cohort is prohibitively expensive or imprac…
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…