552 citations · 1.1k across the 22 of their papers we have counts for
23 papers
Machine Learning with a Reject Option: A survey
Kilian Hendrickx, Lorenzo Perini, Dries Van der Plas +2
Machine learning models always make a prediction, even when it is likely to be inaccurate. This behavior should be avoided in many decision support applications, where mistakes can…
Leaving Goals on the Pitch: Evaluating Decision Making in Soccer
Maaike Van Roy, Pieter Robberechts, Wen-Chi Yang +2
Analysis of the popular expected goals (xG) metric in soccer has determined that a (slightly) smaller number of high-quality attempts will likely yield more goals than a slew of lo…
Versatile Verification of Tree Ensembles
Laurens Devos, Wannes Meert, Jesse Davis
Machine learned models often must abide by certain requirements (e.g., fairness or legal). This has spurred interested in developing approaches that can provably verify whether a m…
Using Machine Learning and Alternative Data to Predict Movements in Market Risk
Thomas Dierckx, Jesse Davis, Wim Schoutens
Using machine learning and alternative data for the prediction of financial markets has been a popular topic in recent years. Many financial variables such as stock price, historic…
A general anomaly detection framework for fleet-based condition monitoring of machines
Kilian Hendrickx, Wannes Meert, Yves Mollet +4
Machine failures decrease up-time and can lead to extra repair costs or even to human casualties and environmental pollution. Recent condition monitoring techniques use artificial…
Verifying Tree Ensembles by Reasoning about Potential Instances
Laurens Devos, Wannes Meert, Jesse Davis
Imagine being able to ask questions to a black box model such as "Which adversarial examples exist?", "Does a specific attribute have a disproportionate effect on the model's predi…