Partial Order MCMC for Structure Discovery in Bayesian Networks. - Teppo Niinimaki; Pekka Parviainen; Mikko Koivisto (UAI, 2011)

Local Structure Discovery in Bayesian Networks. - Teppo Niinimaki; Pekka Parviainen (UAI, 2012)

Annealed Importance Sampling for Structure Learning in Bayesian Networks. - Teppo Mikael Niinimäki; Mikko Koivisto (IJCAI, 2013)

Treedy: A Heuristic for Counting and Sampling Subsets. - Teppo Niinimaki; Mikko Koivisto (UAI, 2013)

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)

Structure Discovery in Bayesian Networks by Sampling Partial Orders. - Teppo Niinimaki; Pekka Parviainen; Mikko Koivisto (J. Mach. Learn. Res., 2016)

The Mixing of Markov Chains on Linear Extensions in Practice. - Topi Talvitie; Teppo Mikael Niinimäki; Mikko Koivisto (IJCAI, 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)

Representation transfer for differentially private drug sensitivity prediction. - Teppo Mikael Niinimäki; Mikko A. Heikkilä; Antti Honkela; Samuel Kaski (Bioinform., 2019)