Viktoria Korchemna

Projektass.in(FWF) Dipl.-Ing.in / BSc

Viktoria Korchemna

Role

PreDoc Researcher
Algorithms and Complexity, E192-01

Contact

Publications

The Complexity of Fair Division of Indivisible Items with Externalities / Deligkas, A., Eiben, E., Korchemna, V., & Schierreich, Š. (2024). The Complexity of Fair Division of Indivisible Items with Externalities. In Proceedings of the 38th AAAI Conference on Artificial Intelligence (pp. 9653–9661). AAAI Press. https://doi.org/10.1609/aaai.v38i9.28822
Slim Tree-Cut Width / Ganian, R., & Korchemna, V. (2024). Slim Tree-Cut Width. Algorithmica, 86(8), 2714–2738. https://doi.org/10.1007/s00453-024-01241-4
The Fine-Grained Complexity of Graph Homomorphism Parameterized by Clique-Width / Ganian, R., Hamm, T., Korchemna, V., Okrasa, K., & Simonov, K. (2024). The Fine-Grained Complexity of Graph Homomorphism Parameterized by Clique-Width. ACM Transactions on Algorithms, 20(3). https://doi.org/10.1145/3652514
Counting Vanishing Matrix-Vector Products / Brand, C., Korchemna, V., Simonov, K., & Skotnica, M. (2024). Counting Vanishing Matrix-Vector Products. In R. Uehara, K. Yamanaka, & H.-C. Yen (Eds.), WALCOM: Algorithms and Computation: 18th International Conference and Workshops on Algorithms and Computation, WALCOM 2024, Kanazawa, Japan, March 18-20, 2024, Proceedings (pp. 335–349). http://hdl.handle.net/20.500.12708/204115
Revisiting Causal Discovery from a Complexity-Theoretic Perspective / Ganian, R., Korchemna, V., & Szeider, S. (2024). Revisiting Causal Discovery from a Complexity-Theoretic Perspective. In K. Larson (Ed.), Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence (pp. 3377–3385). International Joint Conferences on Artificial Intelligence.
Deterministic Constrained Multilinear Detection / Brand, C., Korchemna, V., & Skotnica, M. (2023). Deterministic Constrained Multilinear Detection. In J. Leroux, S. Lombardy, & D. Peleg (Eds.), 48th International Symposium on Mathematical Foundations of Computer Science (MFCS 2023) (pp. 1–14). Schloss-Dagstuhl - Leibniz Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.MFCS.2023.25
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Project: Parameterisierte Analyse in der Künstlichen Intelligenz (2021–2026)
A Structural Complexity Analysis of Synchronous Dynamical Systems / Eiben, E., Ganian, R., Hamm, T., & Korchemna, V. (2023). A Structural Complexity Analysis of Synchronous Dynamical Systems. In B. Williams, Y. Chen, & J. Neville (Eds.), Proceedings of the 37th AAAI Conference on Artificial Intelligence (pp. 6313–6321). AAAI Press. https://doi.org/10.1609/aaai.v37i5.25777
The Fine-Grained Complexity of Graph Homomorphism Parameterized by Clique-Width / Ganian, R., Hamm, T., Korchemna, V., Okrasa, K., & Simonov, K. (2022). The Fine-Grained Complexity of Graph Homomorphism Parameterized by Clique-Width. In 49th EATCS International Conference on Automata, Languages, and Programming (pp. 66:1-66:20). Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH. https://doi.org/10.4230/LIPIcs.ICALP.2022.66
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Projects: NFPC (2018–2022) / Parameterisierte Analyse in der Künstlichen Intelligenz (2021–2026)
Slim Tree-Cut Width / Ganian, R., & Korchemna, V. (2022). Slim Tree-Cut Width. In H. Dell & J. Nederlof (Eds.), 17th International Symposium on Parameterized and Exact Computation (IPEC 2022) (pp. 1–18). Schloss Dagstuhl -- Leibniz-Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.IPEC.2022.15
The Complexity of k-Means Clustering when Little is Known / Ganian, R., Hamm, T., Korchemna, V., Okrasa, K., & Simonov, K. (2022). The Complexity of k-Means Clustering when Little is Known. In Proceedings of the 39th International Conference on Machine Learning (pp. 6960–6987). https://doi.org/10.34726/3070
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Projects: NFPC (2018–2022) / Parameterisierte Analyse in der Künstlichen Intelligenz (2021–2026)
Edge-Cut Width: An Algorithmically Driven Analogue of Treewidth Based on Edge Cuts / Brand, C., Ceylan, E., Ganian, R., Hatschka, C., & Korchemna, V. (2022). Edge-Cut Width: An Algorithmically Driven Analogue of Treewidth Based on Edge Cuts. In M. A. Bekos & M. Kaufmann (Eds.), Graph-Theoretic Concepts in Computer Science (pp. 98–113). Springer Nature Switzerland AG. https://doi.org/10.1007/978-3-031-15914-5_8
The Complexity of Bayesian Network Learning: Revisiting the Superstructure / Ganian, R., & Korchemna, V. (2021). The Complexity of Bayesian Network Learning: Revisiting the Superstructure. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P. S. Liang, & J. Wortman Vaughan (Eds.), Advances in Neural Information Processing Systems 34 (NeurIPS 2021) (pp. 430–442). Curran Associates, Inc. https://doi.org/10.34726/3905
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Projects: NFPC (2018–2022) / Parameterisierte Analyse in der Künstlichen Intelligenz (2021–2026)
Parameterized algorithms for Bayesian network learning / Korchemna, V. (2021). Parameterized algorithms for Bayesian network learning [Diploma Thesis, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2021.90847
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