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Marcel Wienöbst
Marcel Wienöbst
Verified email at tcs.uni-luebeck.de - Homepage
Title
Cited by
Cited by
Year
Polynomial-time algorithms for counting and sampling Markov equivalent dags
M Wienöbst, M Bannach, M Liskiewicz
Proceedings of the AAAI Conference on Artificial Intelligence 35 (13), 12198 …, 2021
252021
Extendability of causal graphical models: Algorithms and computational complexity
M Wienöbst, M Bannach, M Liskiewicz
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial …, 2021
162021
Recovering causal structures from low-order conditional independencies
M Wienöbst, M Liskiewicz
Proceedings of the AAAI Conference on Artificial Intelligence 34 (06), 10302 …, 2020
62020
Linear-time algorithms for front-door adjustment in causal graphs
M Wienöbst, B van der Zander, M Liśkiewicz
Proceedings of the AAAI Conference on Artificial Intelligence 38 (18), 20577 …, 2024
4*2024
A new constructive criterion for markov equivalence of mags
M Wienöbst, M Bannach, M Liśkiewicz
Uncertainty in Artificial Intelligence, 2107-2116, 2022
32022
PACE Solver Description: PID^⋆
M Bannach, S Berndt, M Schuster, M Wienöbst
15th International Symposium on Parameterized and Exact Computation (IPEC 2020), 2020
32020
Efficient enumeration of Markov equivalent DAGs
M Wienöbst, M Luttermann, M Bannach, M Liskiewicz
Proceedings of the AAAI Conference on Artificial Intelligence 37 (10), 12313 …, 2023
22023
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with Applications
M Wienöbst, M Bannach, M Liśkiewicz
Journal of Machine Learning Research 24 (213), 1-45, 2023
22023
Identification in Tree-shaped Linear Structural Causal Models
B Van Der Zander, M Wienöbst, M Bläser, M Liskiewicz
International Conference on Artificial Intelligence and Statistics, 6770-6792, 2022
12022
An Approach to Reduce the Number of Conditional Independence Tests in the PC Algorithm
M Wienöbst, M Liśkiewicz
KI 2021: Advances in Artificial Intelligence: 44th German Conference on AI …, 2021
12021
PACE solver description: Fluid
M Bannach, S Berndt, M Schuster, M Wienöbst
15th International Symposium on Parameterized and Exact Computation (IPEC 2020), 2020
12020
Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes of DAGs
M Schauer, M Wienöbst
arXiv preprint arXiv:2310.05655, 2023
2023
Practical Algorithms for Orientations of Partially Directed Graphical Models
M Luttermann, M Wienöbst, M Liskiewicz
Conference on Causal Learning and Reasoning, 259-280, 2023
2023
Constraint-based causal structure learning exploiting low-order conditional independences
M Wienöbst, M Liśkiewicz
2019
Experimentelle Analyse von Algorithmen zur Lösung des Bisektionsproblems in Graphen
M Wienöbst, M Liskiewicz
2016
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