| 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 | 27 | 2021 |
| 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 | 19 | 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 | 9 | 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 | 8 | 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 | 4* | 2024 |
| A new constructive criterion for markov equivalence of mags M Wienöbst, M Bannach, M Liśkiewicz Uncertainty in Artificial Intelligence, 2107-2116, 2022 | 4 | 2022 |
| PACE Solver Description: PID^⋆ M Bannach, S Berndt, M Schuster, M Wienöbst 15th International Symposium on Parameterized and Exact Computation (IPEC 2020), 2020 | 3 | 2020 |
| 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 | 2 | 2023 |
| 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 |
| 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 | 2 | 2021 |
| Causal Structure Learning With Momentum: Sampling Distributions Over Markov Equivalence Classes M Schauer, M Wienöbst International Conference on Probabilistic Graphical Models, 382-400, 2024 | 1* | 2024 |
| 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), 2020 | 1 | 2020 |
| 15th International Symposium on Parameterized and Exact Computation (IPEC 2020) A Agrawal, MS Ramanujan, J Bang-Jensen, E Eiben, G Gutin, ... Schloss Dagstuhl-Leibniz-Zentrum für Informatik GmbH, 2020 | | 2020 |
| 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 |
Maciej LiskiewiczUniversity of LübeckVerified email at tcs.uni-luebeck.de
