3 citations · 5 across the 4 of their papers we have counts for
6 papers
Optimizing Treatment Allocation in the Presence of Interference
Daan Caljon, Jente Van Belle, Jeroen Berrevoets +1
In Influence Maximization (IM), the objective is to -- given a budget -- select the optimal set of entities in a network to target with a treatment so as to maximize the total effe…
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…
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…
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…
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…
Causal Simulations for Uplift Modeling
Jeroen Berrevoets, Wouter Verbeke
Uplift modeling requires experimental data, preferably collected in random fashion. This places a logistical and financial burden upon any organisation aspiring such models. Once d…