Generative Relational Learning (Czech Science Foundation Project)

This project is planned to have four main parts. The first part addresses theoretical foundations. The second part is about the framework of Neural Markov Logic Networks that we introduced recently. The third part is the framework of relational variational autoencoders. The fourth part of the project is further development of Lifted Relational Neural Networks and their applications for generative modelling applications. The project runs from 2020 to 2022.

Journal and conference papers

  1. Martin Svatoš, Peter Jung, Jan Tóth, Yuyi Wang and Ondřej Kuželka. On Discovering Interesting Combinatorial Integer Sequences. IJCAI 2023: 32nd International Joint Conference on Artificial Intelligence, 2023. [online]
  2. Yuanhong Wang, Juhua Pu, Yuyi Wang and Ondřej Kuželka. On Exact Sampling in the Two-Variable Fragment of First-Order Logic. LICS 2023: Thirty-Eighth Annual ACM/IEEE Symposium on Logic in Computer Science, 2023. [online] [longer arxiv version]
  3. Jan Tóth and Ondřej Kuželka. Lifted Inference with Linear Order Axiom. AAAI 2023: 37th AAAI Conference on Artificial Intelligence, 2023. [online]
  4. Timothy van Bremen and Ondřej Kuželka. Lifted Inference with Tree Axioms. Artificial Intelligence, 2023 (To appear). [pdf]
  5. Nitesh Kumar, Ondřej Kuželka and Luc De Raedt. First-Order Context-Specific Likelihood Weighting in Hybrid Probabilistic Logic Programs. Journal of Artificial Intelligence Research, 2023. [online]
  6. Ondřej Kuželka. Counting and Sampling Models in First-Order Logic. IJCAI 2023: 32nd International Joint Conference on Artificial Intelligence, 2023. (IJCAI Early Career Spotlight) [online]
  7. Yuanhong Wang, Timothy van Bremen, Yuyi Wang and Ondřej Kuželka. Domain-Lifted Sampling for Universal Two-Variable Logic and Extensions. AAAI 2022: 36th AAAI Conference on Artificial Intelligence, 2022. [online]
  8. Jianhang Ai, Ondřej Kuželka and Yuyi Wang. Hoeffding–Serfling Inequality for U-Statistics Without Replacement. Journal of Theoretical Probability, 2022. [online]
  9. Jianhang Ai, Ondřej Kuželka and Yuyi Wang. Hoeffding and Bernstein Inequalities for U-statistics Without Replacement. Statistics and Probability Letters, 2022. [online]
  10. Ondřej Kuželka. Weighted First-Order Model Counting in the Two-Variable Fragment With Counting Quantifiers. Journal of Artificial Intelligence Research, 2021. [online]
  11. Giuseppe Marra and Ondřej Kuželka. Neural Markov Logic Networks. UAI 2021: 37th Conference on Uncertainty in Artificial Intelligence, 2021. [pdf] [supplement]
  12. Timothy van Bremen and Ondřej Kuželka. Lifted Inference with Tree Axioms. KR 2021: 18th International Conference on Principles of Knowledge Representation and Reasoning, 2021. (Marco Cadoli Best Student Paper Award Runner-up) [online]
  13. Timothy van Bremen and Ondřej Kuželka. Faster Lifting for Two-Variable Logic Using Cell Graphs. UAI 2021: 37th Conference on Uncertainty in Artificial Intelligence, 2021. [pdf]
  14. Jáchym Barvínek, Timothy van Bremen, Yuyi Wang, Filip Železný and Ondřej Kuželka. Automatic Conjecturing of P-Recursions Using Lifted Inference. ILP 2020-21: 30th International Conference on Inductive Logic Programming, 2021. [preliminary version]
  15. Yuanhong Wang, Timothy van Bremen, Juhua Pu, Yuyi Wang and Ondřej Kuželka. Fast Algorithms for Relational Marginal Polytopes. IJCAI 2021: 30th International Joint Conference on Artificial Intelligence, 2021. [online]
  16. Nitesh Kumar, Ondřej Kuželka and Luc De Raedt. Learning Distributional Programs for Relational Autocompletion. Theory and Practice of Logic Programming (accepted). [preliminary version]
  17. Nitesh Kumar and Ondřej Kuželka. Context-Specific Likelihood Weighting. AISTATS 2021: 24th International Conference on Artificial Intelligence and Statistics, 2021. [online]
  18. Gustav Šourek, Filip Železný and Ondřej Kuželka. Lossless Compression of Structured Convolutional Models via Lifting. ICLR 2021: The Ninth International Conference on Learning Representations, 2021. [online]
  19. Gustav Šourek, Filip Železný and Ondřej Kuželka. Beyond Graph Neural Networks with Lifted Relational Neural Networks. Machine Learning, 2021. [online] [preliminary version]
  20. Ondřej Kuželka, Vyacheslav Kungurtsev and Yuyi Wang. Lifted Weight Learning of Markov Logic Networks (Revisited One More Time). PGM 2020: 10th International Conference on Probabilistic Graphical Models, 2020. [online]
  21. Ondřej Kuželka. Complex Markov Logic Networks: Expressivity and Liftability. UAI 2020: 36th Conference on Uncertainty in Artificial Intelligence, 2020. [preliminary version]
  22. Timothy van Bremen and Ondřej Kuželka. Approximate Weighted First-Order Model Counting: Exploiting Fast Approximate Model Counters and Symmetry. IJCAI 2020: 29th International Joint Conference on Artificial Intelligence, 2020. [pdf coming soon]
  23. Ondřej Kuželka and Yuyi Wang. Domain-Liftability of Relational Marginal Polytopes. AISTATS 2020: 23rd International Conference on Artificial Intelligence and Statistics, 2020. [arxiv]
  24. Martin Svatoš, Steven Schockaert, Jesse Davis and Ondřej Kuželka. STRiKE: Rule-Driven Relational Learning Using Stratified k-Entailment. ECAI 2020: 24th European Conference on Artificial Intelligence, 2020. [pdf]

Team Members

  • Ondřej Kuželka (PI, [homepage])
  • Jianhang Ai (PhD student).
  • Jáchym Barvínek (PhD student).
  • Martin Svatoš (PhD student, [homepage]).
  • Gustav Šourek (Postdoc, [homepage]).

Preprints / Technical Reports

  • Ondřej Kuželka. Lifted Inference in 2-Variable Markov Logic Networks with Function and Cardinality Constraints. [arxiv]