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Journal Articles
- 2024
[j13]Anders Hjort, Ida Scheel, Dag Einar Sommervoll, Johan Pensar:
Locally interpretable tree boosting: An application to house price prediction. Decis. Support Syst. 178: 114106 (2024)- [j12]Milena Pavlovic
, Ghadi S. Al Hajj, Chakravarthi Kanduri, Johan Pensar, Mollie Wood, Ludvig Magne Sollid, Victor Greiff, Geir Kjetil Sandve:
Improving generalization of machine learning-identified biomarkers using causal modelling with examples from immune receptor diagnostics. Nat. Mac. Intell. 6(1): 15-24 (2024) - 2021
- [j11]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, Gabriel Balaban, Todd M. Brusko, Maria Chernigovskaya, Scott Christley, Lindsay G. Cowell, Robert Frank, Ivar Grytten, Sveinung Gundersen, Ingrid Hobæk Haff, Eivind Hovig, Ping-Han Hsieh, Günter Klambauer, Marieke L. Kuijjer, Christin Lund-Andersen, Antonio Martini, Thomas Minotto, Johan Pensar, Knut D. Rand, Enrico Riccardi, Philippe A. Robert, Artur Rocha, Andrei Slabodkin, Igor Snapkov, Ludvig Magne Sollid, Dmytro Titov, Cédric R. Weber, Michael Widrich, Gur Yaari, Victor Greiff, Geir Kjetil Sandve:
The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. Nat. Mach. Intell. 3(11): 936-944 (2021) - [j10]Kimmo Suotsalo, Yingying Xu, Jukka Corander, Johan Pensar:
High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood. Stat. Comput. 31(6): 73 (2021) - 2020
- [j9]Johan Pensar, Yingying Xu, Santeri Puranen, Maiju Pesonen, Yoshiyuki Kabashima, Jukka Corander:
High-dimensional structure learning of binary pairwise Markov networks: A comparative numerical study. Comput. Stat. Data Anal. 141: 62-76 (2020) - 2019
- [j8]Jukka Corander, Antti Hyttinen, Juha Kontinen, Johan Pensar, Jouko Väänänen:
A logical approach to context-specific independence. Ann. Pure Appl. Log. 170(9): 975-992 (2019) - 2017
- [j7]Janne Leppä-aho, Johan Pensar, Teemu Roos, Jukka Corander:
Learning Gaussian graphical models with fractional marginal pseudo-likelihood. Int. J. Approx. Reason. 83: 21-42 (2017) - [j6]Yuan Zou, Johan Pensar, Teemu Roos:
Representing local structure in Bayesian networks by Boolean functions. Pattern Recognit. Lett. 95: 73-77 (2017) - [j5]Tomi Janhunen, Martin Gebser, Jussi Rintanen, Henrik J. Nyman, Johan Pensar, Jukka Corander:
Learning discrete decomposable graphical models via constraint optimization. Stat. Comput. 27(1): 115-130 (2017) - 2016
- [j4]Henrik J. Nyman, Jie Xiong, Johan Pensar, Jukka Corander:
Marginal and simultaneous predictive classification using stratified graphical models. Adv. Data Anal. Classif. 10(3): 305-326 (2016) - [j3]Henrik J. Nyman, Johan Pensar, Timo Koski, Jukka Corander:
Context-specific independence in graphical log-linear models. Comput. Stat. 31(4): 1493-1512 (2016) - [j2]Johan Pensar, Henrik J. Nyman, Jarno Lintusaari, Jukka Corander:
The role of local partial independence in learning of Bayesian networks. Int. J. Approx. Reason. 69: 91-105 (2016) - 2015
- [j1]Johan Pensar, Henrik J. Nyman, Timo Koski, Jukka Corander:
Labeled directed acyclic graphs: a generalization of context-specific independence in directed graphical models. Data Min. Knowl. Discov. 29(2): 503-533 (2015)
Conference and Workshop Papers
- 2024
- [c6]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:
Incorporating probabilistic domain knowledge into deep multiple instance learning. ICML 2024 - 2020
- [c5]Johan Pensar, Topi Talvitie, Antti Hyttinen, Mikko Koivisto:
A Bayesian Approach for Estimating Causal Effects from Observational Data. AAAI 2020: 5395-5402 - [c4]Jussi Viinikka, Antti Hyttinen, Johan Pensar, Mikko Koivisto:
Towards Scalable Bayesian Learning of Causal DAGs. NeurIPS 2020 - 2018
- [c3]Antti Hyttinen, Johan Pensar, Juha Kontinen, Jukka Corander:
Structure Learning for Bayesian Networks over Labeled DAGs. PGM 2018: 133-144 - 2016
- [c2]Jukka Corander, Antti Hyttinen, Juha Kontinen, Johan Pensar, Jouko Väänänen:
A Logical Approach to Context-Specific Independence. WoLLIC 2016: 165-182 - 2013
- [c1]Jukka Corander, Tomi Janhunen, Jussi Rintanen, Henrik J. Nyman, Johan Pensar:
Learning Chordal Markov Networks by Constraint Satisfaction. NIPS 2013: 1349-1357
Parts in Books or Collections
- 2016
- [p1]Henrik J. Nyman, Johan Pensar, Jukka Corander:
Context-Specific and Local Independence in Markovian Dependence Structures. Dependence Logic 2016: 219-234
Informal and Other Publications
- 2023
- [i9]Anders Hjort, Gudmund Horn Hermansen, Johan Pensar, Jonathan P. Williams:
Uncertainty quantification in automated valuation models with locally weighted conformal prediction. CoRR abs/2312.06531 (2023) - 2022
- [i8]Milena Pavlovic, Ghadi S. Al Hajj, Johan Pensar, Mollie Wood, Ludvig Magne Sollid, Victor Greiff, Geir Kjetil Sandve:
Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnostics. CoRR abs/2204.09291 (2022) - [i7]Ghadi S. Al Hajj, Johan Pensar, Geir Kjetil Sandve:
DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation. CoRR abs/2205.11234 (2022) - 2020
- [i6]Jussi Viinikka, Antti Hyttinen, Johan Pensar, Mikko Koivisto:
Towards Scalable Bayesian Learning of Causal DAGs. CoRR abs/2010.00684 (2020) - 2019
- [i5]Johan Pensar, Yingying Xu, Santeri Puranen, Maiju Pesonen, Yoshiyuki Kabashima, Jukka Corander:
High-dimensional structure learning of binary pairwise Markov networks: A comparative numerical study. CoRR abs/1901.04345 (2019) - [i4]Juri Kuronen, Jukka Corander, Johan Pensar:
Learning pairwise Markov network structures using correlation neighborhoods. CoRR abs/1910.13832 (2019) - 2016
- [i3]Janne Leppä-aho, Johan Pensar, Teemu Roos, Jukka Corander:
Learning Gaussian Graphical Models With Fractional Marginal Pseudo-likelihood. CoRR abs/1602.07863 (2016) - 2013
- [i2]Jukka Corander, Tomi Janhunen, Jussi Rintanen, Henrik J. Nyman, Johan Pensar:
Learning Chordal Markov Networks by Constraint Satisfaction. CoRR abs/1310.0927 (2013) - [i1]Johan Pensar, Henrik J. Nyman, Timo Koski, Jukka Corander:
Labeled Directed Acyclic Graphs: a generalization of context-specific independence in directed graphical models. CoRR abs/1310.1187 (2013)
Coauthor Index
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