108 citations · 165 across the 21 of their papers we have counts for
13 papers · 1 filter
Beware of "Explanations" of AI
David Martens, Galit Shmueli, Theodoros Evgeniou +14
Understanding the decisions made and actions taken by increasingly complex AI system remains a key challenge. This has led to an expanding field of research in explainable artifici…
Metalearners for Ranking Treatment Effects
Toon Vanderschueren, Wouter Verbeke, Felipe Moraes +1
Efficiently allocating treatments with a budget constraint constitutes an important challenge across various domains. In marketing, for example, the use of promotions to target pot…
Using representation balancing to learn conditional-average dose responses from clustered data
Christopher Bockel-Rickermann, Toon Vanderschueren, Jeroen Berrevoets +2
Estimating a unit's responses to interventions with an associated dose, the "conditional average dose response" (CADR), is relevant in a variety of domains, from healthcare to busi…
Timing Process Interventions with Causal Inference and Reinforcement Learning
Hans Weytjens, Wouter Verbeke, Jochen De Weerdt
The shift from the understanding and prediction of processes to their optimization offers great benefits to businesses and other organizations. Precisely timed process intervention…
Client Recruitment for Federated Learning in ICU Length of Stay Prediction
Vincent Scheltjens, Lyse Naomi Wamba Momo, Wouter Verbeke +1
Machine and deep learning methods for medical and healthcare applications have shown significant progress and performance improvement in recent years. These methods require vast am…
A new perspective on classification: optimally allocating limited resources to uncertain tasks
Toon Vanderschueren, Bart Baesens, Tim Verdonck +1
A central problem in business concerns the optimal allocation of limited resources to a set of available tasks, where the payoff of these tasks is inherently uncertain. In credit c…
To do or not to do: cost-sensitive causal decision-making
Diego Olaya, Wouter Verbeke, Jente Van Belle +1
Causal classification models are adopted across a variety of operational business processes to predict the effect of a treatment on a categorical business outcome of interest depen…
HydaLearn: Highly Dynamic Task Weighting for Multi-task Learning with Auxiliary Tasks
Sam Verboven, Muhammad Hafeez Chaudhary, Jeroen Berrevoets +1
Multi-task learning (MTL) can improve performance on a task by sharing representations with one or more related auxiliary-tasks. Usually, MTL-networks are trained on a composite lo…
Misclassification cost-sensitive ensemble learning: A unifying framework
George Petrides, Wouter Verbeke
Over the years, a plethora of cost-sensitive methods have been proposed for learning on data when different types of misclassification errors incur different costs. Our contributio…
The foundations of cost-sensitive causal classification
Wouter Verbeke, Diego Olaya, Jeroen Berrevoets +2
Classification is a well-studied machine learning task which concerns the assignment of instances to a set of outcomes. Classification models support the optimization of managerial…
Autoencoders for strategic decision support
Sam Verboven, Jeroen Berrevoets, Chris Wuytens +2
In the majority of executive domains, a notion of normality is involved in most strategic decisions. However, few data-driven tools that support strategic decision-making are avail…
Learning to rank for uplift modeling
Floris Devriendt, Tias Guns, Wouter Verbeke
Uplift modeling has effectively been used in fields such as marketing and customer retention, to target those customers that are most likely to respond due to the campaign or treat…
Optimising Individual-Treatment-Effect Using Bandits
Jeroen Berrevoets, Sam Verboven, Wouter Verbeke
Applying causal inference models in areas such as economics, healthcare and marketing receives great interest from the machine learning community. In particular, estimating the ind…