552 citations · 564 across the 5 of their papers we have counts for
5 papers
ProbLog4Fairness: A Neurosymbolic Approach to Modeling and Mitigating Bias
Rik Adriaensen, Lucas Van Praet, Jessa Bekker +3
Operationalizing definitions of fairness is difficult in practice, as multiple definitions can be incompatible while each being arguably desirable. Instead, it may be easier to dir…
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…
Measuring Adverse Drug Effects on Multimorbity using Tractable Bayesian Networks
Jessa Bekker, Arjen Hommersom, Martijn Lappenschaar +1
Managing patients with multimorbidity often results in polypharmacy: the prescription of multiple drugs. However, the long-term effects of specific combinations of drugs and diseas…