| 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 | 32 | 2021 |
| Extendability of causal graphical models: Algorithms and computational complexity M Wienöbst, M Bannach, M Liśkiewicz Uncertainty in Artificial Intelligence, 1248-1257, 2021 | 23 | 2021 |
| Recovering causal structures from low-order conditional independencies M Wienöbst, M Liskiewicz Proceedings of the AAAI Conference on Artificial Intelligence 34 (06), 10302 …, 2020 | 14 | 2020 |
| 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 | 10 | 2023 |
| 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 | 9* | 2024 |
| 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 | 9 | 2023 |
| A new constructive criterion for markov equivalence of mags M Wienöbst, M Bannach, M Liśkiewicz Uncertainty in Artificial Intelligence, 2107-2116, 2022 | 7 | 2022 |
| Causal structure learning with momentum: Sampling distributions over Markov Equivalence Classes M Schauer, M Wienöbst Proceedings of Machine Learning Research 246, 382-400, 2024 | 3* | 2024 |
| PACE solver description: UzL exact solver for one-sided crossing minimization M Bannach, F Chudigiewitsch, KM Klein, M Wienöbst 19th International Symposium on Parameterized and Exact Computation (IPEC …, 2024 | 3 | 2024 |
| An approach to reduce the number of conditional independence tests in the pc algorithm M Wienöbst, M Liśkiewicz German Conference on Artificial Intelligence (Künstliche Intelligenz), 276-288, 2021 | 3 | 2021 |
| PACE Solver Description: PID^⋆ M Bannach, S Berndt, M Schuster, M Wienöbst 15th International Symposium on Parameterized and Exact Computation (IPEC …, 2020 | 3 | 2020 |
| 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 | 2 | 2022 |
| Practical algorithms for orientations of partially directed graphical models M Luttermann, M Wienöbst, M Liskiewicz Conference on Causal Learning and Reasoning, 259-280, 2023 | 1 | 2023 |
| PACE solver description: Fluid M Bannach, S Berndt, M Schuster, M Wienöbst 15th International Symposium on Parameterized and Exact Computation (IPEC …, 2020 | 1 | 2020 |
| Embracing Discrete Search: A Reasonable Approach to Causal Structure Learning M Wienöbst, L Henckel, S Weichwald arXiv preprint arXiv:2510.04970, 2025 | | 2025 |
| Linear-Time Primitives for Algorithm Development in Graphical Causal Inference M Wienöbst, S Weichwald, L Henckel arXiv preprint arXiv:2506.15758, 2025 | | 2025 |
| PACE Solver Description: UzL Solver for Dominating Set and Hitting Set M Bannach, F Chudigiewitsch, M Wienöbst 20th International Symposium on Parameterized and Exact Computation (IPEC …, 2025 | | 2025 |
| CausalInference. jl M Schauer, M Keller, M Wienöbst Zenodo, 2024 | | 2024 |
| Algorithms for Markov Equivalence M Wienöbst Zentrale Hochschulbibliothek Lübeck, 2024 | | 2024 |
| Constraint-based causal structure learning exploiting low-order conditional independences M Wienöbst, M Liśkiewicz | | 2019 |
Maciej LiskiewiczUniversity of LübeckVerified email at tcs.uni-luebeck.de
