Johan Pensar - RankMe

33 papers A* 3A 1C 1Journal 23Unranked 4

Year	Rank	Type	Title / Venue / Authors
2025	A	`conf`	Causal Inference amid Missingness-Specific Independences and Mechanism Shifts. UAI Johan de Aguas, Leonard Henckel, Johan Pensar, Guido Biele
2025	—	`conf`	The Probability of Tiered Benefit: Partial Identification with Robust and Stable Inference. CLeaR Johan de Aguas, Sebastian Krumscheid, Johan Pensar, Guido Biele
2025	J	`jnl`	Uncertainty quantification in automated valuation models with spatially weighted conformal prediction. Int. J. Data Sci. Anal. Anders Hjort, Gudmund Horn Hermansen, Johan Pensar, Jonathan P. Williams
2024	—	`conf`	Clustered Conformal Prediction for the Housing Market. COPA Anders Hjort, Jonathan P. Williams, Johan Pensar
2024	J	`jnl`	Improving generalization of machine learning-identified biomarkers using causal modelling with examples from immune receptor diagnostics. Nat. Mac. Intell. Milena Pavlovic, Ghadi S. Al Hajj, Chakravarthi Kanduri, Johan Pensar, Mollie Wood, Ludvig Magne Sollid, Victor Greiff, Geir Kjetil Sandve
2024	A*	`conf`	Incorporating probabilistic domain knowledge into deep multiple instance learning. ICML 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
2024	J	`jnl`	Locally interpretable tree boosting: An application to house price prediction. Decis. Support Syst. Anders Hjort, Ida Scheel, Dag Einar Sommervoll, Johan Pensar
2023	J	`jnl`	Uncertainty quantification in automated valuation models with locally weighted conformal prediction. CoRR Anders Hjort, Gudmund Horn Hermansen, Johan Pensar, Jonathan P. Williams
2022	J	`jnl`	DagSim: Combining DAG-based model structure with unconstrained data types and relations for flexible, transparent, and modularized data simulation. CoRR Ghadi S. Al Hajj, Johan Pensar, Geir Kjetil Sandve
2022	J	`jnl`	Improving generalization of machine learning-identified biomarkers with causal modeling: an investigation into immune receptor diagnostics. CoRR Milena Pavlovic, Ghadi S. Al Hajj, Johan Pensar, Mollie Wood, Ludvig Magne Sollid, Victor Greiff, Geir Kjetil Sandve
2021	J	`jnl`	High-dimensional structure learning of sparse vector autoregressive models using fractional marginal pseudo-likelihood. Stat. Comput. Kimmo Suotsalo, Yingying Xu, Jukka Corander, Johan Pensar
2021	J	`jnl`	The immuneML ecosystem for machine learning analysis of adaptive immune receptor repertoires. Nat. Mach. Intell. 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
2020	A*	`conf`	A Bayesian Approach for Estimating Causal Effects from Observational Data. AAAI Johan Pensar, Topi Talvitie, Antti Hyttinen, Mikko Koivisto
2020	J	`jnl`	High-dimensional structure learning of binary pairwise Markov networks: A comparative numerical study. Comput. Stat. Data Anal. Johan Pensar, Yingying Xu, Santeri Puranen, Maiju Pesonen, Yoshiyuki Kabashima, Jukka Corander
2020	A*	`conf`	Towards Scalable Bayesian Learning of Causal DAGs. NeurIPS Jussi Viinikka, Antti Hyttinen, Johan Pensar, Mikko Koivisto
2020	J	`jnl`	Towards Scalable Bayesian Learning of Causal DAGs. CoRR Jussi Viinikka, Antti Hyttinen, Johan Pensar, Mikko Koivisto
2019	J	`jnl`	A logical approach to context-specific independence. Ann. Pure Appl. Log. Jukka Corander, Antti Hyttinen, Juha Kontinen, Johan Pensar, Jouko Väänänen
2019	J	`jnl`	High-dimensional structure learning of binary pairwise Markov networks: A comparative numerical study. CoRR Johan Pensar, Yingying Xu, Santeri Puranen, Maiju Pesonen, Yoshiyuki Kabashima, Jukka Corander
2019	J	`jnl`	Learning pairwise Markov network structures using correlation neighborhoods. CoRR Juri Kuronen, Jukka Corander, Johan Pensar
2018	—	`conf`	Structure Learning for Bayesian Networks over Labeled DAGs. PGM Antti Hyttinen, Johan Pensar, Juha Kontinen, Jukka Corander
2017	J	`jnl`	Learning Gaussian graphical models with fractional marginal pseudo-likelihood. Int. J. Approx. Reason. Janne Leppä-aho, Johan Pensar, Teemu Roos, Jukka Corander
2017	J	`jnl`	Learning discrete decomposable graphical models via constraint optimization. Stat. Comput. Tomi Janhunen, Martin Gebser, Jussi Rintanen, Henrik J. Nyman, Johan Pensar, Jukka Corander
2017	J	`jnl`	Representing local structure in Bayesian networks by Boolean functions. Pattern Recognit. Lett. Yuan Zou, Johan Pensar, Teemu Roos
2016	C	`conf`	A Logical Approach to Context-Specific Independence. WoLLIC Jukka Corander, Antti Hyttinen, Juha Kontinen, Johan Pensar, Jouko Väänänen
2016	—	`ch.`	Context-Specific and Local Independence in Markovian Dependence Structures. Dependence Logic Henrik J. Nyman, Johan Pensar, Jukka Corander
2016	J	`jnl`	Context-specific independence in graphical log-linear models. Comput. Stat. Henrik J. Nyman, Johan Pensar, Timo Koski, Jukka Corander
2016	J	`jnl`	Learning Gaussian Graphical Models With Fractional Marginal Pseudo-likelihood. CoRR Janne Leppä-aho, Johan Pensar, Teemu Roos, Jukka Corander
2016	J	`jnl`	Marginal and simultaneous predictive classification using stratified graphical models. Adv. Data Anal. Classif. Henrik J. Nyman, Jie Xiong, Johan Pensar, Jukka Corander
2016	J	`jnl`	The role of local partial independence in learning of Bayesian networks. Int. J. Approx. Reason. Johan Pensar, Henrik J. Nyman, Jarno Lintusaari, Jukka Corander
2015	J	`jnl`	Labeled directed acyclic graphs: a generalization of context-specific independence in directed graphical models. Data Min. Knowl. Discov. Johan Pensar, Henrik J. Nyman, Timo Koski, Jukka Corander
2013	J	`jnl`	Labeled Directed Acyclic Graphs: a generalization of context-specific independence in directed graphical models. CoRR Johan Pensar, Henrik J. Nyman, Timo Koski, Jukka Corander
2013	—	`conf`	Learning Chordal Markov Networks by Constraint Satisfaction. NIPS Jukka Corander, Tomi Janhunen, Jussi Rintanen, Henrik J. Nyman, Johan Pensar
2013	J	`jnl`	Learning Chordal Markov Networks by Constraint Satisfaction. CoRR Jukka Corander, Tomi Janhunen, Jussi Rintanen, Henrik J. Nyman, Johan Pensar