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Johan Pensar
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2020 – today
- 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
Improving generalization of machine learning-identified biomarkers using causal modelling with examples from immune receptor diagnostics. Nat. Mac. Intell. 6(1): 15-24 (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 - 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) - 2021
- [j11]Milena Pavlovic
The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. Nat. Mach. Intell. 3(11): 936-944 (2021) - [j10]Kimmo Suotsalo
High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood. Stat. Comput. 31(6): 73 (2021) - 2020
- [j9]Johan Pensar
High-dimensional structure learning of binary pairwise Markov networks: A comparative numerical study. Comput. Stat. Data Anal. 141: 62-76 (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 - [i6]Jussi Viinikka, Antti Hyttinen, Johan Pensar, Mikko Koivisto:
Towards Scalable Bayesian Learning of Causal DAGs. CoRR abs/2010.00684 (2020)
2010 – 2019
- 2019
- [j8]Jukka Corander, Antti Hyttinen
A logical approach to context-specific independence. Ann. Pure Appl. Log. 170(9): 975-992 (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
Learning pairwise Markov network structures using correlation neighborhoods. CoRR abs/1910.13832 (2019) - 2018
- [c3]Antti Hyttinen, Johan Pensar, Juha Kontinen, Jukka Corander:
Structure Learning for Bayesian Networks over Labeled DAGs. PGM 2018: 133-144 - 2017
- [j7]Janne Leppä-aho, Johan Pensar
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
Learning discrete decomposable graphical models via constraint optimization. Stat. Comput. 27(1): 115-130 (2017) - 2016
- [j4]Henrik J. Nyman, Jie Xiong, Johan Pensar
Marginal and simultaneous predictive classification using stratified graphical models. Adv. Data Anal. Classif. 10(3): 305-326 (2016) - [j3]Henrik J. Nyman, Johan Pensar
Context-specific independence in graphical log-linear models. Comput. Stat. 31(4): 1493-1512 (2016) - [j2]Johan Pensar
The role of local partial independence in learning of Bayesian networks. Int. J. Approx. Reason. 69: 91-105 (2016) - [c2]Jukka Corander, Antti Hyttinen
A Logical Approach to Context-Specific Independence. WoLLIC 2016: 165-182 - [p1]Henrik J. Nyman, Johan Pensar, Jukka Corander:
Context-Specific and Local Independence in Markovian Dependence Structures. Dependence Logic 2016: 219-234 - [i3]Janne Leppä-aho, Johan Pensar, Teemu Roos, Jukka Corander:
Learning Gaussian Graphical Models With Fractional Marginal Pseudo-likelihood. CoRR abs/1602.07863 (2016) - 2015
- [j1]Johan Pensar
Labeled directed acyclic graphs: a generalization of context-specific independence in directed graphical models. Data Min. Knowl. Discov. 29(2): 503-533 (2015) - 2013
- [c1]Jukka Corander, Tomi Janhunen, Jussi Rintanen, Henrik J. Nyman, Johan Pensar:
Learning Chordal Markov Networks by Constraint Satisfaction. NIPS 2013: 1349-1357 - [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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