Johan Pensar

Orcid: 0000-0002-5158-5761

According to our database1, Johan Pensar authored at least 21 papers between 2013 and 2024.

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Bibliography

2024
Locally interpretable tree boosting: An application to house price prediction.
Decis. Support Syst., March, 2024

2023
Uncertainty quantification in automated valuation models with locally weighted conformal prediction.
CoRR, 2023

2022
DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation.
CoRR, 2022

Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnostics.
CoRR, 2022

2021
High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood.
Stat. Comput., 2021

The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires.
Nat. Mach. Intell., 2021

2020
High-dimensional structure learning of binary pairwise Markov networks: A comparative numerical study.
Comput. Stat. Data Anal., 2020

Towards Scalable Bayesian Learning of Causal DAGs.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

A Bayesian Approach for Estimating Causal Effects from Observational Data.
Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

2019
Learning pairwise Markov network structures using correlation neighborhoods.
CoRR, 2019

A logical approach to context-specific independence.
Ann. Pure Appl. Log., 2019

2018
Structure Learning for Bayesian Networks over Labeled DAGs.
Proceedings of the International Conference on Probabilistic Graphical Models, 2018

2017
Learning discrete decomposable graphical models via constraint optimization.
Stat. Comput., 2017

Representing local structure in Bayesian networks by Boolean functions.
Pattern Recognit. Lett., 2017

Learning Gaussian graphical models with fractional marginal pseudo-likelihood.
Int. J. Approx. Reason., 2017

2016
The role of local partial independence in learning of Bayesian networks.
Int. J. Approx. Reason., 2016

Context-specific independence in graphical log-linear models.
Comput. Stat., 2016

Marginal and simultaneous predictive classification using stratified graphical models.
Adv. Data Anal. Classif., 2016

Context-Specific and Local Independence in Markovian Dependence Structures.
Proceedings of the Dependence Logic, Theory and Applications, 2016

2015
Labeled directed acyclic graphs: a generalization of context-specific independence in directed graphical models.
Data Min. Knowl. Discov., 2015

2013
Learning Chordal Markov Networks by Constraint Satisfaction.
Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013


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