Predicting the Hardness of Learning Bayesian Networks. - Brandon M. Malone; Kustaa Kangas; Matti Järvisalo; Mikko Koivisto; Petri Myllymäki (AAAI, 2014)

Learning Chordal Markov Networks by Dynamic Programming. - Kustaa Kangas; Mikko Koivisto; Teppo Mikael Niinimäki (NIPS, 2014)

Averaging of Decomposable Graphs by Dynamic Programming and Sampling. - Kustaa Kangas; Teppo Mikael Niinimäki; Mikko Koivisto (UAI, 2015)

Counting Linear Extensions of Sparse Posets. - Kustaa Kangas; Teemu Hankala; Teppo Mikael Niinimäki; Mikko Koivisto (IJCAI, 2016)

Combinatorial Algorithms with Applications in Learning Graphical Models. - Kustaa Kangas (2016)

AS-ASL: Algorithm Selection with Auto-sklearn. - Brandon M. Malone; Kustaa Kangas; Matti Järvisalo; Mikko Koivisto; Petri Myllymäki (OASC, 2017)

Counting Linear Extensions in Practice: MCMC Versus Exponential Monte Carlo. - Topi Talvitie; Kustaa Kangas; Teppo Mikael Niinimäki; Mikko Koivisto (AAAI, 2018)

A Scalable Scheme for Counting Linear Extensions. - Topi Talvitie; Kustaa Kangas; Teppo Mikael Niinimäki; Mikko Koivisto (IJCAI, 2018)

On the Number of Connected Sets in Bounded Degree Graphs. - Kustaa Kangas; Petteri Kaski; Janne H. Korhonen; Mikko Koivisto (Electron. J. Comb., 2018)

Empirical hardness of finding optimal Bayesian network structures: algorithm selection and runtime prediction. - Brandon M. Malone; Kustaa Kangas; Matti Järvisalo; Mikko Koivisto; Petri Myllymäki (Mach. Learn., 2018)

Counting Linear Extensions: Parameterizations by Treewidth. - Eduard Eiben; Robert Ganian; Kustaa Kangas; Sebastian Ordyniak (Algorithmica, 2019)

A Faster Tree-Decomposition Based Algorithm for Counting Linear Extensions. - Kustaa Kangas; Mikko Koivisto; Sami Salonen (Algorithmica, 2020)