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Samuel Kolb
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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
392018
Learning constraints in spreadsheets and tabular data
S Kolb, S Paramonov, T Guns, L De Raedt
Machine Learning 106, 1441-1468, 2017
352017
Efficient Symbolic Integration for Probabilistic Inference.
S Kolb, M Mladenov, S Sanner, V Belle, K Kersting
IJCAI, 5031-5037, 2018
272018
Learning constraints and optimization criteria
SM Kolb
Workshops at the Thirtieth AAAI Conference on Artificial Intelligence, 2016
182016
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
112019
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
112018
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
102020
Learning MAX-SAT from contextual examples for combinatorial optimisation
M Kumar, S Kolb, S Teso, L De Raedt
Artificial Intelligence 314, 103794, 2023
92023
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
82020
Learning linear programs from data
EA Schede, S Kolb, S Teso
2019 IEEE 31st International Conference on Tools with Artificial …, 2019
72019
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
72017
Predictive spreadsheet autocompletion with constraints
S Kolb, S Teso, A Dries, L De Raedt
Machine Learning 109, 307-325, 2020
62020
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
42021
Hybrid Probabilistic Inference with Logical and Algebraic Constraints: a Survey.
P Morettin, PZ Dos Martires, S Kolb, A Passerini
IJCAI, 4533-4542, 2021
42021
Learning mixed-integer linear programs from contextual examples
M Kumar, S Kolb, L De Raedt, S Teso
arXiv preprint arXiv:2107.07136, 2021
32021
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
22020
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
12021
Monte carlo anti-differentiation for approximate weighted model integration
PZD Martires, S Kolb
arXiv preprint arXiv:2001.04566, 2020
12020
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
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