14 citations · 50 across the 20 of their papers we have counts for
22 papers
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…
Qini Curve Estimation under Clustered Network Interference
Rickard Karlsson, Bram van den Akker, Felipe Moraes +2
Qini curves are a widely used tool for assessing treatment policies under allocation constraints as they visualize the incremental gain of a new treatment policy versus the cost of…
Falsification of Unconfoundedness by Testing Independence of Causal Mechanisms
Rickard K. A. Karlsson, Jesse H. Krijthe
A major challenge in estimating treatment effects in observational studies is the reliance on untestable conditions such as the assumption of no unmeasured confounding. In this wor…
Robust integration of external control data in randomized trials
Rickard Karlsson, Guanbo Wang, Piersilvio De Bartolomeis +2
One approach for increasing the efficiency of randomized trials is the use of "external controls" -- individuals who received the control treatment studied in the trial during rout…
The risks of risk assessment: causal blind spots when using prediction models for treatment decisions
Nan van Geloven, Ruth H Keogh, Wouter van Amsterdam +12
Clinicians increasingly rely on prediction models to guide treatment choices. Most prediction models, however, are developed using observational data that include some patients who…
When accurate prediction models yield harmful self-fulfilling prophecies
Wouter A. C. van Amsterdam, Nan van Geloven, Jesse H. Krijthe +2
Prediction models are popular in medical research and practice. By predicting an outcome of interest for specific patients, these models may help inform difficult treatment decisio…
Risk-based decision making: estimands for sequential prediction under interventions
Kim Luijken, Paweł Morzywołek, Wouter van Amsterdam +14
Prediction models are used amongst others to inform medical decisions on interventions. Typically, individuals with high risks of adverse outcomes are advised to undergo an interve…
Detecting hidden confounding in observational data using multiple environments
Rickard K. A. Karlsson, Jesse H. Krijthe
A common assumption in causal inference from observational data is that there is no hidden confounding. Yet it is, in general, impossible to verify this assumption from a single da…
ReproducedPapers.org: Openly teaching and structuring machine learning reproducibility
Burak Yildiz, Hayley Hung, Jesse H. Krijthe +9
We present ReproducedPapers.org: an open online repository for teaching and structuring machine learning reproducibility. We evaluate doing a reproduction project among students an…
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…
Reproducible Pattern Recognition Research: The Case of Optimistic SSL
Jesse H. Krijthe, Marco Loog
In this paper, we discuss the approaches we took and trade-offs involved in making a paper on a conceptual topic in pattern recognition research fully reproducible. We discuss our…
RSSL: Semi-supervised Learning in R
Jesse H. Krijthe
In this paper, we introduce a package for semi-supervised learning research in the R programming language called RSSL. We cover the purpose of the package, the methods it includes…
The Pessimistic Limits and Possibilities of Margin-based Losses in Semi-supervised Learning
Jesse H. Krijthe, Marco Loog
Consider a classification problem where we have both labeled and unlabeled data available. We show that for linear classifiers defined by convex margin-based surrogate losses that…
The Peaking Phenomenon in Semi-supervised Learning
Jesse H. Krijthe, Marco Loog
For the supervised least squares classifier, when the number of training objects is smaller than the dimensionality of the data, adding more data to the training set may first incr…
Optimistic Semi-supervised Least Squares Classification
Jesse H. Krijthe, Marco Loog
The goal of semi-supervised learning is to improve supervised classifiers by using additional unlabeled training examples. In this work we study a simple self-learning approach to…
Projected Estimators for Robust Semi-supervised Classification
Jesse H. Krijthe, Marco Loog
For semi-supervised techniques to be applied safely in practice we at least want methods to outperform their supervised counterparts. We study this question for classification usin…
Robust Semi-supervised Least Squares Classification by Implicit Constraints
Jesse H. Krijthe, Marco Loog
We introduce the implicitly constrained least squares (ICLS) classifier, a novel semi-supervised version of the least squares classifier. This classifier minimizes the squared loss…
Feature-Level Domain Adaptation
Wouter M. Kouw, Jesse H. Krijthe, Marco Loog +1
Domain adaptation is the supervised learning setting in which the training and test data are sampled from different distributions: training data is sampled from a source domain, wh…
Implicitly Constrained Semi-Supervised Least Squares Classification
Jesse H. Krijthe, Marco Loog
We introduce a novel semi-supervised version of the least squares classifier. This implicitly constrained least squares (ICLS) classifier minimizes the squared loss on the labeled…
Implicitly Constrained Semi-Supervised Linear Discriminant Analysis
Jesse H. Krijthe, Marco Loog
Semi-supervised learning is an important and active topic of research in pattern recognition. For classification using linear discriminant analysis specifically, several semi-super…