| Learning from positive and unlabeled data: A survey J Bekker, J Davis Machine Learning 109 (4), 719-760, 2020 | 554 | 2020 |
| Estimating the class prior in positive and unlabeled data through decision tree induction J Bekker, J Davis Proceedings of the AAAI conference on artificial intelligence 32 (1), 2018 | 128 | 2018 |
| Learning the structure of probabilistic sentential decision diagrams Y Liang, J Bekker, G Van den Broeck Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence …, 2017 | 107 | 2017 |
| Beyond the selected completely at random assumption for learning from positive and unlabeled data J Bekker, P Robberechts, J Davis Joint European conference on machine learning and knowledge discovery in …, 2019 | 99 | 2019 |
| Tractable learning for complex probability queries J Bekker, J Davis, A Choi, A Darwiche, G Van den Broeck Advances in Neural Information Processing Systems 28, 2015 | 67 | 2015 |
| Learning from positive and unlabeled data under the selected at random assumption J Bekker, J Davis Second International Workshop on Learning with Imbalanced Domains: Theory …, 2018 | 19 | 2018 |
| Positive and unlabeled relational classification through label frequency estimation J Bekker, J Davis Inductive Logic Programming: 27th International Conference, ILP 2017 …, 2018 | 11 | 2018 |
| A scalable ensemble approach to forecast the electricity consumption of households L Botman, J Soenen, K Theodorakos, A Yurtman, J Bekker, ... IEEE Transactions on Smart Grid 14 (1), 757-768, 2022 | 8 | 2022 |
| Learning from positive and unlabeled data J Bekker | 5 | 2018 |
| Interactive multi-level prosody control for expressive speech synthesis T Cornille, F Wang, J Bekker ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and …, 2022 | 4 | 2022 |
| Unifying knowledge base completion with pu learning to mitigate the observation bias J Schouterden, J Bekker, J Davis, H Blockeel Proceedings of the AAAI conference on artificial intelligence 36 (4), 4137-4145, 2022 | 3 | 2022 |
| Modeling PU learning using probabilistic logic programming V Verreet, L De Raedt, J Bekker Machine Learning, 1-22, 2023 | 1 | 2023 |
| Bagging Propensity Weighting: A Robust method for biased PU Learning S De Block, J Bekker Fourth International Workshop on Learning with Imbalanced Domains: Theory …, 2022 | 1 | 2022 |
| Measuring adverse drug effects on multimorbity using tractable bayesian networks J Bekker, A Hommersom, M Lappenschaar, J Davis arXiv preprint arXiv:1612.03055, 2016 | 1 | 2016 |
| Optimizing workforce allocation under uncertain activity duration V Derkinderen, J Bekker, P Smet Computers & Industrial Engineering 179, 109228, 2023 | | 2023 |
| Measuring Adverse Drug Effects on Multimorbity using Tractable Bayesian Networks.: Machine Learning for Health J Bekker, AJ Hommersom, M Lappenschaar, JGE Janzing Neural Information Processing Systems, 2016 | | 2016 |
| Learning the Structure of Probabilistic SDDs J Bekker, A Choi, G Van den Broeck Women in Machine Learning, 2016 | | 2016 |
| Ordering-based search for tractable Bayesian networks J Bekker, G Van den Broeck, J Davis Women in Machine Learning, Date: 2015/12/07-2015/12/07, Location: Montreal, 2015 | | 2015 |
| Inferring Missing CV Skills using PU Learning and Variational Inference V Verreet, L De Smet, R Manhaeve, P Delobelle, J Bekker | | |
| Replication Data for: Optimizing Workforce Allocation under Uncertain Activity Duration V DERKINDEREN, J BEKKER, P SMET KU Leuven RDR, 0 | | |
Jesse DavisProfessor, Department of Computer Science, KU LeuvenVerified email at cs.kuleuven.be
