TU Wien Informatics

20 Years

Role

2023W

2024S

 

  • Proven Optimally-Balanced Latin Rectangles with SAT / Ramaswamy, V. P., & Szeider, S. (2023). Proven Optimally-Balanced Latin Rectangles with SAT. In R. Yap (Ed.), 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Schloss-Dagstuhl - Leibniz Zentrum für Informatik. https://doi.org/10.4230/LIPIcs.CP.2023.48
    Download: PDF (551 KB)
    Projects: REVEAL-AI (2020–2024) / STRIDES (2023–2026)
  • Scalable Bayesian network structure learning using SAT-based methods / Peruvemba Ramaswamy, V. (2023). Scalable Bayesian network structure learning using SAT-based methods [Dissertation, Technische Universität Wien]. reposiTUm. https://doi.org/10.34726/hss.2023.113249
    Download: PDF (3.17 MB)
  • Learning Large Bayesian Networks with Expert Constraints / Peruvemba Ramaswamy, V., & Szeider, S. (2022). Learning Large Bayesian Networks with Expert Constraints. In Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI 2022) (pp. 1592–1601). PMLR. https://doi.org/10.34726/3821
    Download: PDF (606 KB)
  • Learning Fast-Inference Bayesian Networks / Peruvemba Ramaswamy, V., & Szeider, S. (2022). Learning Fast-Inference Bayesian Networks. In Advances in Neural Information Processing Systems 34 (NeurIPS 2021). 35th conference on neural information processing systems (NeurIPS 2021), International. https://doi.org/10.34726/4023
    Download: PDF (1.07 MB)
    Projects: REVEAL-AI (2020–2024) / SLIM (2019–2024)
  • Turbocharging Treewidth-Bounded Bayesian Network Structure Learning / Ramaswamy, V. P., & Szeider, S. (2021). Turbocharging Treewidth-Bounded Bayesian Network Structure Learning. In Thirty-Fifth AAAI Conference on Artificial Intelligence (pp. 3895–3903). AAAI Press. http://hdl.handle.net/20.500.12708/58598
  • MaxSAT-Based Postprocessing for Treedepth / Peruvemba Ramaswamy, V., & Szeider, S. (2020). MaxSAT-Based Postprocessing for Treedepth. In Lecture Notes in Computer Science (pp. 478–495). LNCS. https://doi.org/10.1007/978-3-030-58475-7_28