Search Results for author:
Found 5 papers, 2 papers with code
Learning from positive and unlabeled data: a survey
Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data.
Beyond the Selected Completely At Random Assumption for Learning from Positive and Unlabeled Data
Most positive and unlabeled data is subject to selection biases.
Learning from Positive and Unlabeled Data under the Selected At Random Assumption
Experiments show that our method is not only very capable of learning under this assumption, but it also outperforms the state of the art for learning under the selected completely at random assumption.
Measuring Adverse Drug Effects on Multimorbity using Tractable Bayesian Networks
Our results confirm that prescriptions may lead to unintended negative consequences in further development of multimorbidity in cardiovascular diseases.
Tractable Learning for Complex Probability Queries
We propose a tractable learner that guarantees efficient inference for a broader class of queries.
