Representative publications:
Empirical Hardness of Finding Optimal Bayesian Network Structures: Algorithm Selection and Runtime Prediction. Brandon Malone, Kustaa Kangas, Matti Järvisalo, Mikko Koivisto, and Petri Myllymäki. Machine Learning 107(1):247-283, 2018.
[doi:10.1007/s10994-017-5680-2] [pdf] [abstract/bibtex]
Bayesian Network Structure Learning with Integer Programming: Polytopes, Facets and Complexity. James Cussens, Matti Järvisalo, Janne H. Korhonen, and Mark Bartlett. Journal of Artificial Intelligence Research 58:185-229, 2017.
[doi:10.1613/jair.5203] [pdf] [abstract/bibtex]
Learning Chordal Markov Networks via Branch and Bound. Kari Rantanen, Antti Hyttinen, and Matti Järvisalo. In Isabelle Guyon, Ulrike von Luxburg, Samy Bengio, Hanna M. Wallach, Rob Fergus, S. V. N. Vishwanathan, and Roman Garnett, editors, Proceedings of the 30th Annual Conference on Neural Information Processing Systems (NIPS 2017), pages 1845-1855, 2017.
[doi:???] [pdf] [abstract/bibtex]
A Core-Guided Approach to Learning Optimal Causal Graphs. Antti Hyttinen, Paul Saikko, and Matti Järvisalo. In Carles Sierra, editor, Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI 2017), pages 645-651. AAAI Press, 2017.
[doi:10.24963/ijcai.2017/90] [pdf] [abstract/bibtex]
Learning Optimal Bounded Treewidth Bayesian Networks via Maximum Satisfiability. Jeremias Berg, Matti Järvisalo, and Brandon Malone. In Jukka Corander and Samuel Kaski, editors, Proceedings of the 17th International Conference on Artificial Intelligence and Statistics (AISTATS 2014), volume 33 of JMLR Workshop and Conference Proceedings, pages 86-95. JMLR, 2014.
[Publisher's version] [pdf] [abstract/bibtex]