14 citations · 50 across the 20 of their papers we have counts for
4 papers · 1 filter
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…
A Brief Prehistory of Double Descent
Marco Loog, Tom Viering, Alexander Mey +2
In their thought-provoking paper [1], Belkin et al. illustrate and discuss the shape of risk curves in the context of modern high-complexity learners. Given a fixed training sample…
On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL
Marco Loog, Jesse H. Krijthe, Are C. Jensen
In various approaches to learning, notably in domain adaptation, active learning, learning under covariate shift, semi-supervised learning, learning with concept drift, and the lik…
Nuclear Discrepancy for Active Learning
Tom J. Viering, Jesse H. Krijthe, Marco Loog
Active learning algorithms propose which unlabeled objects should be queried for their labels to improve a predictive model the most. We study active learners that minimize general…