Learning Chordal Markov Networks by Constraint Satisfaction. - Jukka Corander; Tomi Janhunen; Jussi Rintanen; Henrik J. Nyman; Johan Pensar (NIPS, 2013)

Labeled directed acyclic graphs: a generalization of context-specific independence in directed graphical models. - Johan Pensar; Henrik J. Nyman; Timo Koski; Jukka Corander (Data Min. Knowl. Discov., 2015)

Context-Specific and Local Independence in Markovian Dependence Structures. - Henrik J. Nyman; Johan Pensar; Jukka Corander (Dependence Logic, 2016)

Marginal and simultaneous predictive classification using stratified graphical models. - Henrik J. Nyman; Jie Xiong; Johan Pensar; Jukka Corander (Adv. Data Anal. Classif., 2016)

Context-specific independence in graphical log-linear models. - Henrik J. Nyman; Johan Pensar; Timo Koski; Jukka Corander (Comput. Stat., 2016)

The role of local partial independence in learning of Bayesian networks. - Johan Pensar; Henrik J. Nyman; Jarno Lintusaari; Jukka Corander (Int. J. Approx. Reason., 2016)

Learning Gaussian graphical models with fractional marginal pseudo-likelihood. - Janne Leppä-aho; Johan Pensar; Teemu Roos; Jukka Corander (Int. J. Approx. Reason., 2017)

Representing local structure in Bayesian networks by Boolean functions. - Yuan Zou; Johan Pensar; Teemu Roos (Pattern Recognit. Lett., 2017)

Learning discrete decomposable graphical models via constraint optimization. - Tomi Janhunen; Martin Gebser; Jussi Rintanen; Henrik J. Nyman; Johan Pensar; Jukka Corander (Stat. Comput., 2017)

Structure Learning for Bayesian Networks over Labeled DAGs. - Antti Hyttinen; Johan Pensar; Juha Kontinen; Jukka Corander (PGM, 2018)

A logical approach to context-specific independence. - Jukka Corander; Antti Hyttinen; Juha Kontinen; Johan Pensar; Jouko Väänänen (Ann. Pure Appl. Log., 2019)

Learning pairwise Markov network structures using correlation neighborhoods. - Juri Kuronen; Jukka Corander; Johan Pensar (CoRR, 2019)

A Bayesian Approach for Estimating Causal Effects from Observational Data. - Johan Pensar; Topi Talvitie; Antti Hyttinen; Mikko Koivisto (AAAI, 2020)

Towards Scalable Bayesian Learning of Causal DAGs. - Jussi Viinikka; Antti Hyttinen; Johan Pensar; Mikko Koivisto (NeurIPS, 2020)

High-dimensional structure learning of binary pairwise Markov networks: A comparative numerical study. - Johan Pensar; Yingying Xu; Santeri Puranen; Maiju Pesonen; Yoshiyuki Kabashima; Jukka Corander (Comput. Stat. Data Anal., 2020)

The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. - Milena Pavlovic; Lonneke Scheffer; Keshav Motwani; Chakravarthi Kanduri; Radmila Kompova; Nikolay Vazov; Knut Waagan; Fabian L. M. Bernal; Alexandre Almeida Costa; Brian Corrie; Rahmad Akbar; Ghadi S. Al Hajj; et al. (Nat. Mach. Intell., 2021)

High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood. - Kimmo Suotsalo; Yingying Xu; Jukka Corander; Johan Pensar (Stat. Comput., 2021)

Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnostics. - Milena Pavlovic; Ghadi S. Al Hajj; Johan Pensar; Mollie Wood; Ludvig Magne Sollid; Victor Greiff; Geir Kjetil Sandve (CoRR, 2022)

DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation. - Ghadi S. Al Hajj; Johan Pensar; Geir Kjetil Sandve (CoRR, 2022)

Uncertainty quantification in automated valuation models with locally weighted conformal prediction. - Anders Hjort; Gudmund Horn Hermansen; Johan Pensar; Jonathan P. Williams (CoRR, 2023)

Clustered Conformal Prediction for the Housing Market. - Anders Hjort; Jonathan P. Williams; Johan Pensar (COPA, 2024)

Incorporating probabilistic domain knowledge into deep multiple instance learning. - Ghadi S. Al Hajj; Aliaksandr Hubin; Chakravarthi Kanduri; Milena Pavlovic; Knut Dagestad Rand; Michael Widrich; Anne H. Schistad Solberg; Victor Greiff; Johan Pensar; Günter Klambauer; Geir Kjetil Sandve (ICML, 2024)

Improving generalization of machine learning-identified biomarkers using causal modelling with examples from immune receptor diagnostics. - Milena Pavlovic; Ghadi S. Al Hajj; Chakravarthi Kanduri; Johan Pensar; Mollie Wood; Ludvig Magne Sollid; Victor Greiff; Geir Kjetil Sandve (Nat. Mac. Intell., 2024)

Locally interpretable tree boosting: An application to house price prediction. - Anders Hjort; Ida Scheel; Dag Einar Sommervoll; Johan Pensar (Decis. Support Syst., 2024)

The Probability of Tiered Benefit: Partial Identification with Robust and Stable Inference. - Johan de Aguas; Sebastian Krumscheid; Johan Pensar; Guido Biele (CLeaR, 2025)

Causal Inference amid Missingness-Specific Independences and Mechanism Shifts. - Johan de Aguas; Leonard Henckel; Johan Pensar; Guido Biele (UAI, 2025)