| Learning SMT (LRA) constraints using SMT solvers S Kolb, S Teso, A Passerini, L De Raedt IJCAI International Joint Conference on Artificial Intelligence 2018, 2333-2340, 2018 | 39 | 2018 |
| Learning constraints in spreadsheets and tabular data S Kolb, S Paramonov, T Guns, L De Raedt Machine Learning 106, 1441-1468, 2017 | 35 | 2017 |
| Efficient Symbolic Integration for Probabilistic Inference. S Kolb, M Mladenov, S Sanner, V Belle, K Kersting IJCAI, 5031-5037, 2018 | 27 | 2018 |
| Learning constraints and optimization criteria SM Kolb Workshops at the Thirtieth AAAI Conference on Artificial Intelligence, 2016 | 18 | 2016 |
| The pywmi framework and toolbox for probabilistic inference using weighted model integration S Kolb, P Morettin, P Zuidberg Dos Martires, F Sommavilla, A Passerini, ... Proceedings of the twenty-Eighth International Joint Conference on …, 2019 | 11 | 2019 |
| Elements of an automatic data scientist L De Raedt, H Blockeel, S Kolb, S Teso, G Verbruggen Advances in Intelligent Data Analysis XVII: 17th International Symposium …, 2018 | 11 | 2018 |
| How to exploit structure while solving weighted model integration problems S Kolb, PZ Dos Martires, L De Raedt Uncertainty in Artificial Intelligence, 744-754, 2020 | 10 | 2020 |
| Learning MAX-SAT from contextual examples for combinatorial optimisation M Kumar, S Kolb, S Teso, L De Raedt Artificial Intelligence 314, 103794, 2023 | 9 | 2023 |
| Learning weighted model integration distributions P Morettin, S Kolb, S Teso, A Passerini Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 5224-5231, 2020 | 8 | 2020 |
| Learning linear programs from data EA Schede, S Kolb, S Teso 2019 IEEE 31st International Conference on Tools with Artificial …, 2019 | 7 | 2019 |
| Tacle: Learning constraints in tabular data S Paramonov, S Kolb, T Guns, L De Raedt Proceedings of the 2017 ACM on Conference on Information and Knowledge …, 2017 | 7 | 2017 |
| Predictive spreadsheet autocompletion with constraints S Kolb, S Teso, A Dries, L De Raedt Machine Learning 109, 307-325, 2020 | 6 | 2020 |
| Zuidberg Dos Martires, P.; and De Raedt, L. 2019. How to exploit structure while solving weighted model integration problems S Kolb Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence …, 0 | 6 | |
| for Democratizing Data Science C Gautrais, Y Dauxais, S Teso, S Kolb, G Verbruggen, L De Raedt Human-Like Machine Intelligence, 379, 2021 | 4 | 2021 |
| Hybrid Probabilistic Inference with Logical and Algebraic Constraints: a Survey. P Morettin, PZ Dos Martires, S Kolb, A Passerini IJCAI, 4533-4542, 2021 | 4 | 2021 |
| Learning mixed-integer linear programs from contextual examples M Kumar, S Kolb, L De Raedt, S Teso arXiv preprint arXiv:2107.07136, 2021 | 3 | 2021 |
| Ordering variables for weighted model integration V Derkinderen, E Heylen, PZ Dos Martires, S Kolb, L Raedt Conference on Uncertainty in Artificial Intelligence, 879-888, 2020 | 2 | 2020 |
| Democratizing constraint satisfaction problems through machine learning M Kumar, S Kolb, C Gautrais, L De Raedt Proceedings of the AAAI Conference on Artificial Intelligence 35 (18), 16057 …, 2021 | 1 | 2021 |
| Monte carlo anti-differentiation for approximate weighted model integration PZD Martires, S Kolb arXiv preprint arXiv:2001.04566, 2020 | 1 | 2020 |
| Learning Constraint Programming Models from Data Using Generate-And-Aggregate M Kumar, S Kolb, T Guns 28th International Conference on Principles and Practice of Constraint …, 2022 | | 2022 |
