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Samuel Kolb
Samuel Kolb
Geverifieerd e-mailadres voor kuleuven.be
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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
402018
Learning constraints in spreadsheets and tabular data
S Kolb, S Paramonov, T Guns, L De Raedt
Machine Learning 106 (9), 1441-1468, 2017
342017
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
Elements of an automatic data scientist
L De Raedt, H Blockeel, S Kolb, S Teso, G Verbruggen
International symposium on intelligent data analysis, 3-14, 2018
122018
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
Learning MAX-SAT from contextual examples for combinatorial optimisation
M Kumar, S Kolb, S Teso, L De Raedt
Artificial Intelligence 314, 103794, 2023
102023
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 linear programs from data
EA Schede, S Kolb, S Teso
2019 IEEE 31st International Conference on Tools with Artificial …, 2019
82019
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
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
62020
Predictive spreadsheet autocompletion with constraints
S Kolb, S Teso, A Dries, L De Raedt
Machine Learning 109 (2), 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, P Zuidberg Dos Martires, S Kolb, A Passerini
Proceedings of the 30th International Joint Conference on Artificial …, 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
22021
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
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
Learning MAX-SAT Models from Examples using Genetic Algorithms and Knowledge Compilation
S Berden, M Kumar, S Kolb, T Guns
28th International Conference on Principles and Practice of Constraint …, 2022
2022
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Artikelen 1–20