Laurens Devos
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Safety Verification of Tree-Ensemble Policies via Predicate Abstraction Open
Learned action policies are gaining traction in AI, but come without safety guarantees. Recent work devised a method for safety verification of neural policies via predicate abstraction. Here we extend this approach to policies represented…
Robustness Verification of Multi-Class Tree Ensembles Open
Tree ensembles are one of the most widely used model classes. However, these models are susceptible to adversarial examples, which are slightly perturbed examples that elicit a misprediction. There has been significant research on designin…
Faster Repeated Evasion Attacks in Tree Ensembles Open
Tree ensembles are one of the most widely used model classes. However, these models are susceptible to adversarial examples, i.e., slightly perturbed examples that elicit a misprediction. There has been significant research on designing ap…
Decision trees: from efficient prediction to responsible AI Open
This article provides a birds-eye view on the role of decision trees in machine learning and data science over roughly four decades. It sketches the evolution of decision tree research over the years, describes the broader context in which…
Adversarial Example Detection in Deployed Tree Ensembles Open
Tree ensembles are powerful models that are widely used. However, they are susceptible to adversarial examples, which are examples that purposely constructed to elicit a misprediction from the model. This can degrade performance and erode …
Verifying Tree Ensembles by Reasoning about Potential Instances Open
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 prediction?" or "What kind of predictions could possibly be made…
Gradient boosting for quantitative finance Open
status: Published
Gradient Boosting for Quantitative Finance Open
In this paper, we discuss how tree-based machine learning techniques can be used in the context of derivatives pricing. Gradient boosted regression trees are employed to learn the pricing map for a couple of classical, time-consuming probl…
Versatile Verification of Tree Ensembles Open
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 model satisfies certain properties. This paper introduces a …
Additive Tree Ensembles: Reasoning About Potential Instances. Open
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 prediction? or What kind of predictions are possible for a partiall…