Viktoria Korchemna
Projektass.in(FWF) Dipl.-Ing.in / BSc
Role
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PreDoc Researcher
Algorithms and Complexity, E192-01
Publications
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Deterministic Constrained Multilinear Detection
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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
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The Fine-Grained Complexity of Graph Homomorphism Parameterized by Clique-Width
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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) -
The Complexity of k-Means Clustering when Little is Known
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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) - 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
- 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
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The Complexity of Bayesian Network Learning: Revisiting the Superstructure
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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
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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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