552 citations · 1.1k across the 22 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…
LazyBum: Decision tree learning using lazy propositionalization
Jonas Schouterden, Jesse Davis, Hendrik Blockeel
Propositionalization is the process of summarizing relational data into a tabular (attribute-value) format. The resulting table can next be used by any propositional learner. This…
A Bayesian Approach to In-Game Win Probability in Soccer
Pieter Robberechts, Jan Van Haaren, Jesse Davis
In-game win probability models, which provide a sports team's likelihood of winning at each point in a game based on historical observations, are becoming increasingly popular. In…
Learning from positive and unlabeled data: a survey
Jessa Bekker, Jesse Davis
Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabele…
Beyond the Selected Completely At Random Assumption for Learning from Positive and Unlabeled Data
Jessa Bekker, Pieter Robberechts, Jesse Davis
Most positive and unlabeled data is subject to selection biases. The labeled examples can, for example, be selected from the positive set because they are easier to obtain or more…
Learning from Positive and Unlabeled Data under the Selected At Random Assumption
Jessa Bekker, Jesse Davis
For many interesting tasks, such as medical diagnosis and web page classification, a learner only has access to some positively labeled examples and many unlabeled examples. Learni…
Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation
Kendrick Boyd, Vitor Santos Costa, Jesse Davis +1
Precision-recall (PR) curves and the areas under them are widely used to summarize machine learning results, especially for data sets exhibiting class skew. They are often used ana…
Demand-Driven Clustering in Relational Domains for Predicting Adverse Drug Events
Jesse Davis, Vitor Santos Costa, Peggy Peissig +3
Learning from electronic medical records (EMR) is challenging due to their relational nature and the uncertain dependence between a patient's past and future health status. Statist…