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Tom Heskes

the Alzheimer's Disease Neuroimaging Initiatives, the MASTERPLAN Study Group, the OPTIMISTIC Consortium

Learning Equational Theorem Proving


Feb 10, 2021
Jelle Piepenbrock, Tom Heskes, Mikoláš Janota, Josef Urban

* 17 pages, 4 figures 

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Inferring the Direction of a Causal Link and Estimating Its Effect via a Bayesian Mendelian Randomization Approach


Dec 18, 2020
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes

* Statistical Methods in Medical Research, Vol 29, Issue 4, 2020 
* 26 pages, 22 figures, published in Statistical Methods in Medical Research 

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MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models


Dec 18, 2020
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes

* PMLR 124:1049-1058, 2020 
* 14 pages, 7 figures, Published in the Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI) 

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Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models


Nov 03, 2020
Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen

* Accepted at 34th Conference on Neural Information Processing Systems (NeurIPS 2020) 

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Large-Scale Local Causal Inference of Gene Regulatory Relationships


Sep 10, 2019
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes

* 32 pages, 9 figures, 2 tables. This manuscript version has been accepted for publication in the International Journal of Approximate Reasoning. It incorporates reviewer comments and has a new title. This manuscript constitutes an extended version of a previous paper shared on arXiv (arXiv:1809.06827) that has been published in the proceedings of the PGM 2018 conference 

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Constraining the Parameters of High-Dimensional Models with Active Learning


May 19, 2019
Sascha Caron, Tom Heskes, Sydney Otten, Bob Stienen


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A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks


Sep 18, 2018
Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen, Tom Heskes

* PMLR 72 (2018) 37-48 
* 12 pages, 4 figures, 3 tables 

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A Novel Bayesian Approach for Latent Variable Modeling from Mixed Data with Missing Values


Jun 12, 2018
Ruifei Cui, Ioan Gabriel Bucur, Perry Groot, Tom Heskes


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Stable specification search in structural equation model with latent variables


May 24, 2018
Ridho Rahmadi, Perry Groot, Tom Heskes


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Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness


Apr 06, 2017
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes

* PMLR 54:1523-1531, 2017 
* 10 pages, 12 figures, Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) 2017 

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Causality on Longitudinal Data: Stable Specification Search in Constrained Structural Equation Modeling


Apr 04, 2017
Ridho Rahmadi, Perry Groot, Marieke HC van Rijn, Jan AJG van den Brand, Marianne Heins, Hans Knoop, Tom Heskes


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Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities


Oct 29, 2016
Mohsen Ghafoorian, Nico Karssemeijer, Tom Heskes, Inge van Uden, Clara Sanchez, Geert Litjens, Frank-Erik de Leeuw, Bram van Ginneken, Elena Marchiori, Bram Platel

* 13 pages, 8 figures 

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Deep Multi-scale Location-aware 3D Convolutional Neural Networks for Automated Detection of Lacunes of Presumed Vascular Origin


Oct 29, 2016
Mohsen Ghafoorian, Nico Karssemeijer, Tom Heskes, Mayra Bergkamp, Joost Wissink, Jiri Obels, Karlijn Keizer, Frank-Erik de Leeuw, Bram van Ginneken, Elena Marchiori, Bram Platel

* Neuroimage Clin 14 (2017) 391-399 
* 11 pages, 7 figures 

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The Artificial Mind's Eye: Resisting Adversarials for Convolutional Neural Networks using Internal Projection


Jul 14, 2016
Harm Berntsen, Wouter Kuijper, Tom Heskes


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Causality on Cross-Sectional Data: Stable Specification Search in Constrained Structural Equation Modeling


Jul 14, 2016
Ridho Rahmadi, Perry Groot, Marianne Heins, Hans Knoop, Tom Heskes

* Applied.Soft.Comp. 52 (2017) 687-698 

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Regularizing Solutions to the MEG Inverse Problem Using Space-Time Separable Covariance Functions


Apr 17, 2016
Arno Solin, Pasi Jylänki, Jaakko Kauramäki, Tom Heskes, Marcel A. J. van Gerven, Simo Särkkä

* 25 pages, 7 figures 

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Bigger Buffer k-d Trees on Multi-Many-Core Systems


Dec 09, 2015
Fabian Gieseke, Cosmin Eugen Oancea, Ashish Mahabal, Christian Igel, Tom Heskes


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Sparse Approximate Inference for Spatio-Temporal Point Process Models


Jul 06, 2015
Botond Cseke, Andrew Zammit Mangion, Tom Heskes, Guido Sanguinetti


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Proof Supplement - Learning Sparse Causal Models is not NP-hard (UAI2013)


Nov 06, 2014
Tom Claassen, Joris M. Mooij, Tom Heskes

* 11 pages, supplement to `Learning Sparse Causal Models is not NP-hard' (UAI2013) 

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Properties of Bethe Free Energies and Message Passing in Gaussian Models


Jan 16, 2014
Botond Cseke, Tom Heskes

* Journal Of Artificial Intelligence Research, Volume 41, pages 1-24, 2011 

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Cyclic Causal Discovery from Continuous Equilibrium Data


Sep 26, 2013
Joris Mooij, Tom Heskes

* Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013) 

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Learning Sparse Causal Models is not NP-hard


Sep 26, 2013
Tom Claassen, Joris Mooij, Tom Heskes

* Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013) 

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Semi-supervised Ranking Pursuit


Jul 02, 2013
Evgeni Tsivtsivadze, Tom Heskes


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IPF for Discrete Chain Factor Graphs


Dec 12, 2012
Wim Wiegerinck, Tom Heskes

* Appears in Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI2002) 

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Expectation Propogation for approximate inference in dynamic Bayesian networks


Dec 12, 2012
Tom Heskes, Onno Zoeter

* Appears in Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI2002) 

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Approximate Inference and Constrained Optimization


Oct 19, 2012
Tom Heskes, Kees Albers, Hilbert Kappen

* Appears in Proceedings of the Nineteenth Conference on Uncertainty in Artificial Intelligence (UAI2003) 

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A Bayesian Approach to Constraint Based Causal Inference


Oct 16, 2012
Tom Claassen, Tom Heskes

* Appears in Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial Intelligence (UAI2012) 

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Bounds on the Bethe Free Energy for Gaussian Networks


Jun 13, 2012
Botond Cseke, Tom Heskes

* Appears in Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial Intelligence (UAI2008) 

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Premise Selection for Mathematics by Corpus Analysis and Kernel Methods


Apr 12, 2012
Jesse Alama, Tom Heskes, Daniel Kühlwein, Evgeni Tsivtsivadze, Josef Urban

* 26 pages 

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A Logical Characterization of Constraint-Based Causal Discovery


Feb 14, 2012
Tom Claassen, Tom Heskes


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