Retrieval of pattern sequences at variable speeds in a neural network with delays. - Tom Heskes; Stan C. A. M. Gielen (Neural Networks, 1992)

Error potentials for self-organization. - Tom Heskes; Bert Kappen (ICNN, 1993)

Stochastics of on-line back-propagation. - Tom Heskes (ESANN, 1994)

A Neural Model of Visual Attention. - Piërre van de Laar; Tom Heskes; Stan C. A. M. Gielen (SNN Symposium on Neural Networks, 1995)

Output Coding and Modularity for Multi-Class Problems. - André Pastoors; Tom Heskes (SNN Symposium on Neural Networks, 1995)

Practical Confidence and Prediction Intervals. - Tom Heskes (NIPS, 1996)

Balancing Between Bagging and Bumping. - Tom Heskes (NIPS, 1996)

Transition times in self-organizing maps. - Tom Heskes (Biol. Cybern., 1996)

How Dependencies between Successive Examples Affect On-Line Learning. - Wim Wiegerinck; Tom Heskes (Neural Comput., 1996)

A theoretical comparison of batch-mode, on-line, cyclic, and almost-cyclic learning. - Tom Heskes; Wim Wiegerinck (IEEE Trans. Neural Networks, 1996)

Input Selection with Partial Retraining. - Piërre van de Laar; Stan C. A. M. Gielen; Tom Heskes (ICANN, 1997)

Selecting Weighting Factors in Logarithmic Opinion Pools. - Tom Heskes (NIPS, 1997)

Task-Dependent Learning of Attention. - Piërre van de Laar; Tom Heskes; Stan C. A. M. Gielen (Neural Networks, 1997)

Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach. - Tom Heskes (ICML, 1998)

Bias/Variance Decompositions for Likelihood-Based Estimators. - Tom Heskes (Neural Comput., 1998)

Model clustering by deterministic annealing. - Bart Bakker; Tom Heskes (ESANN, 1999)

Partial Retraining: A New Approach to Input Relevance Determination. - Piërre van de Laar; Tom Heskes; Stan C. A. M. Gielen (Int. J. Neural Syst., 1999)

Pruning Using Parameter and Neuronal Metrics. - Piërre van de Laar; Tom Heskes (Neural Comput., 1999)

Survival Analysis: A Neural-Bayesian Approach. - Bart J. Bakker; Bert Kappen; Tom Heskes (ANNIMAB, 2000)

Empirical Bayes for Learning to Learn. - Tom Heskes (ICML, 2000)

General Bias/Variance Decomposition with Target Independent Variance of Error Functions Derived from the Exponential Family of Distributions. - Jakob Vogdrup Hansen; Tom Heskes (ICPR, 2000)

EM Algorithms for Self-Organizing Maps. - Tom Heskes; Jan-Joost Spanjers; Wim Wiegerinck (IJCNN (6), 2000)

Input selection based on an ensemble. - Piërre van de Laar; Tom Heskes (Neurocomputing, 2000)

On "Natural" Learning and Pruning in Multilayered Perceptrons. - Tom Heskes (Neural Comput., 2000)

Self-organizing maps, vector quantization, and mixture modeling. - Tom Heskes (IEEE Trans. Neural Networks, 2001)

Model Clustering for Neural Network Ensembles. - Bart Bakker; Tom Heskes (ICANN, 2002)

Automatic Categorization of Web Pages and User Clustering with Mixtures of Hidden Markov Models. - Alexander Ypma; Tom Heskes (WEBKDD, 2002)

Stable Fixed Points of Loopy Belief Propagation Are Local Minima of the Bethe Free Energy. - Tom Heskes (NIPS, 2002)

Fractional Belief Propagation. - Wim Wiegerinck; Tom Heskes (NIPS, 2002)

Expectation Propogation for Approximate Inference in Dynamic Bayesian Networks. - Tom Heskes; Onno Zoeter (UAI, 2002)

IPF for Discrete Chain Factor Graphs. - Wim Wiegerinck; Tom Heskes (UAI, 2002)

Approximate algorithms for neural-Bayesian approaches. - Tom Heskes; Bart Bakker; Bert Kappen (Theor. Comput. Sci., 2002)

Generalized belief propagation for approximate inference in hybrid Bayesian networks. - Tom Heskes; Onno Zoeter (AISTATS, 2003)

Multi-scale Switching Linear Dynamical Systems. - Onno Zoeter; Tom Heskes (ICANN, 2003)

Approximate Expectation Maximization. - Tom Heskes; Onno Zoeter; Wim Wiegerinck (NIPS, 2003)

Iterated extended Kalman smoothing with expectation-propagation. - Alexander Ypma; Tom Heskes (NNSP, 2003)

Approximate Inference and Constrained Optimization. - Tom Heskes; Kees Albers; Bert Kappen (UAI, 2003)

Task Clustering and Gating for Bayesian Multitask Learning. - Bart Bakker; Tom Heskes (J. Mach. Learn. Res., 2003)

Optimising newspaper sales using neural-Bayesian technology. - Tom Heskes; Jan-Joost Spanjers; Bart Bakker; Wim Wiegerinck (Neural Comput. Appl., 2003)

Clustering ensembles of neural network models. - Bart Bakker; Tom Heskes (Neural Networks, 2003)

Hierarchical Visualization of Time-Series Data Using Switching Linear Dynamical Systems. - Onno Zoeter; Tom Heskes (IEEE Trans. Pattern Anal. Mach. Intell., 2003)

Novel approximations for inference and learning in nonlinear dynamical systems. - Alexander Ypma; Tom Heskes (ESANN, 2004)

On the Uniqueness of Loopy Belief Propagation Fixed Points. - Tom Heskes (Neural Comput., 2004)

Gaussian Quadrature Based Expectation Propagation. - Onno Zoeter; Tom Heskes (AISTATS, 2005)

Incremental Utility Elicitation for Adaptive Personalization. - Tom Heskes; Bert de Vries (BNAIC, 2005)

Use of the Noisy Threshold Function in Building Bayesian Networks. - Rasa Jurgelenaite; Peter J. F. Lucas; Tom Heskes (BNAIC, 2005)

Exploring the Noisy Threshold Function in Designing Bayesian Networks. - Rasa Jurgelenaite; Peter J. F. Lucas; Tom Heskes (SGAI Conf., 2005)

Novel approximations for inference in nonlinear dynamical systems using expectation propagation. - Alexander Ypma; Tom Heskes (Neurocomputing, 2005)

Change Point Problems in Linear Dynamical Systems. - Onno Zoeter; Tom Heskes (J. Mach. Learn. Res., 2005)

EM Algorithm for Symmetric Causal Independence Models. - Rasa Jurgelenaite; Tom Heskes (ECML, 2006)

Symmetric Causal Independence Models for Classification. - Rasa Jurgelenaite; Tom Heskes (Probabilistic Graphical Models, 2006)

Convexity Arguments for Efficient Minimization of the Bethe and Kikuchi Free Energies. - Tom Heskes (J. Artif. Intell. Res., 2006)

Deterministic approximate inference techniques for conditionally Gaussian state space models. - Onno Zoeter; Tom Heskes (Stat. Comput., 2006)

Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach. - José Miguel Hernández-Lobato; Tjeerd Dijkstra; Tom Heskes (NIPS, 2007)

Expectation Propagation for Rating Players in Sports Competitions. - Adriana Birlutiu; Tom Heskes (PKDD, 2007)

Predicting carcinoid heart disease with the noisy-threshold classifier. - Marcel van Gerven; Rasa Jurgelenaite; Babs G. Taal; Tom Heskes; Peter J. F. Lucas (Artif. Intell. Medicine, 2007)

Learning and approximate inference in dynamic hierarchical models. - Bart Bakker; Tom Heskes (Comput. Stat. Data Anal., 2007)

Bounds on the Bethe Free Energy for Gaussian Networks. - Botond Cseke; Tom Heskes (UAI, 2008)

Exploring the impact of alternative feature representations on BCI classification. - Ali Bahramisharif; Marcel van Gerven; Tom Heskes (ESANN, 2009)

Multi-task Preference learning with Gaussian Processes. - Adriana Birlutiu; Perry Groot; Tom Heskes (ESANN, 2009)

Bayesian Source Localization with the Multivariate Laplace Prior. - Marcel van Gerven; Botond Cseke; Robert Oostenveld; Tom Heskes (NIPS, 2009)

Gene regulation in the intraerythrocytic cycle of Plasmodium falciparum. - Rasa Jurgelenaite; Tjeerd Dijkstra; Clemens H. M. Kocken; Tom Heskes (Bioinform., 2009)

Interpreting single trial data using groupwise regularisation. - Marcel van Gerven; Christian Hesse; Ole Jensen; Tom Heskes (NeuroImage, 2009)

Selecting features for BCI control based on a covert spatial attention paradigm. - Marcel van Gerven; Ali Bahramisharif; Tom Heskes; Ole Jensen (Neural Networks, 2009)

Co-Regularized Least-Squares for Label Ranking. - Evgeni Tsivtsivadze; Tapio Pahikkala; Jorma Boberg; Tapio Salakoski; Tom Heskes (Preference Learning, 2010)

Bayesian Monte Carlo for the Global Optimization of Expensive Functions. - Perry Groot; Adriana Birlutiu; Tom Heskes (ECAI, 2010)

Causal discovery in multiple models from different experiments. - Tom Claassen; Tom Heskes (NIPS, 2010)

Multi-task preference learning with an application to hearing aid personalization. - Adriana Birlutiu; Perry Groot; Tom Heskes (Neurocomputing, 2010)

Improving posterior marginal approximations in latent Gaussian models. - Botond Cseke; Tom Heskes (AISTATS, 2010)

Neural Decoding with Hierarchical Generative Models. - Marcel van Gerven; Floris P. de Lange; Tom Heskes (Neural Comput., 2010)

Efficient Bayesian multivariate fMRI analysis using a sparsifying spatio-temporal prior. - Marcel A. J. van Gerven; Botond Cseke; Floris P. de Lange; Tom Heskes (NeuroImage, 2010)

Expectation Propagation. - Tom Heskes (Encyclopedia of Machine Learning, 2010)

A structure independent algorithm for causal discovery. - Tom Claassen; Tom Heskes (ESANN, 2011)

Learning of causal relations. - John A. Quinn; Joris M. Mooij; Tom Heskes; Michael Biehl (ESANN, 2011)

Multi-output Ranking for Automated Reasoning. - Daniel Kühlwein; Josef Urban; Evgeni Tsivtsivadze; Herman Geuvers; Tom Heskes (KDIR, 2011)

A Markov Random Field Approach to Neural Encoding and Decoding. - Marcel van Gerven; Eric Maris; Tom Heskes (ICANN (2), 2011)

Learning from Multiple Annotators with Gaussian Processes. - Perry Groot; Adriana Birlutiu; Tom Heskes (ICANN (2), 2011)

Learning2Reason. - Daniel Kühlwein; Josef Urban; Evgeni Tsivtsivadze; Herman Geuvers; Tom Heskes (Calculemus/MKM, 2011)

The Dynamic Beamformer. - Ali Bahramisharif; Marcel A. J. van Gerven; Jan-Mathijs Schoffelen; Zoubin Ghahramani; Tom Heskes (MLINI, 2011)

On Causal Discovery with Cyclic Additive Noise Models. - Joris M. Mooij; Dominik Janzing; Tom Heskes; Bernhard Schölkopf (NIPS, 2011)

Covert Attention as a Paradigm for Subject-Independent Brain-Computer Interfacing. - Hans J. P. Wouters; Marcel A. J. van Gerven; Matthias Sebastian Treder; Tom Heskes; Ali Bahramisharif (MLINI, 2011)

Semantic Graph Kernels for Automated Reasoning. - Evgeni Tsivtsivadze; Josef Urban; Herman Geuvers; Tom Heskes (SDM, 2011)

A Logical Characterization of Constraint-Based Causal Discovery. - Tom Claassen; Tom Heskes (UAI, 2011)

Properties of Bethe Free Energies and Message Passing in Gaussian Models. - Botond Cseke; Tom Heskes (J. Artif. Intell. Res., 2011)

Approximate Marginals in Latent Gaussian Models. - Botond Cseke; Tom Heskes (J. Mach. Learn. Res., 2011)

Dynamic decoding of ongoing perception. - Marcel van Gerven; Peter Kok; Floris P. de Lange; Tom Heskes (NeuroImage, 2011)

Predicting Preference Judgments of Individual Normal and Hearing-Impaired Listeners With Gaussian Processes. - Perry Groot; Tom Heskes; Tjeerd Dijkstra; James M. Kates (IEEE Trans. Speech Audio Process., 2011)

Editorial: One Year as EiC, and Editorial-Board Changes at TNN. - Marco Baglietto; Lubica Benusková; Ivo Bukovsky; Tianping Chen; Tom Heskes; Kazushi Ikeda; Fakhri Karray; Rhee Man Kil; Robert Legenstein; Jinhu Lu; Yunqian Ma; Malik Magdon-Ismail; et al. (IEEE Trans. Neural Networks, 2011)

Overview and Evaluation of Premise Selection Techniques for Large Theory Mathematics. - Daniel Kühlwein; Twan van Laarhoven; Evgeni Tsivtsivadze; Josef Urban; Tom Heskes (IJCAR, 2012)

Online Co-regularized Algorithms. - Tom de Ruijter; Evgeni Tsivtsivadze; Tom Heskes (Discovery Science, 2012)

A Linear Gaussian Framework for Decoding of Perceived Images. - Marcel A. J. van Gerven; Tom Heskes (PRNI, 2012)

A Bayesian Approach to Constraint Based Causal Inference. - Tom Claassen; Tom Heskes (UAI, 2012)

Molecular Machines in the Synapse: Overlapping Protein Sets Control Distinct Steps in Neurosecretion. - L. Niels Cornelisse; Evgeni Tsivtsivadze; Marieke Meijer; Tjeerd Dijkstra; Tom Heskes; Matthijs Verhage (PLoS Comput. Biol., 2012)

Improving Native Language Identification with TF-IDF Weighting. - Binyam Gebrekidan Gebre; Marcos Zampieri; Peter Wittenburg; Tom Heskes (BEA@NAACL-HLT, 2013)

Automatic Signer Diarization - The Mover Is the Signer Approach. - Binyam Gebrekidan Gebre; Peter Wittenburg; Tom Heskes (CVPR Workshops, 2013)

The gesturer is the speaker. - Binyam Gebrekidan Gebre; Peter Wittenburg; Tom Heskes (ICASSP, 2013)

Automatic sign language identification. - Binyam Gebrekidan Gebre; Peter Wittenburg; Tom Heskes (ICIP, 2013)

Bayesian Probabilities for Constraint-Based Causal Discovery. - Tom Claassen; Tom Heskes (IJCAI, 2013)

Multi-view Multi-class Classification for Identification of Pathogenic Bacterial Strains. - Evgeni Tsivtsivadze; Tom Heskes; Armand Paauw (MCS, 2013)

Neighborhood Co-regularized Multi-view Spectral Clustering of Microbiome Data. - Evgeni Tsivtsivadze; Hanneke Borgdorff; Janneke van de Wijgert; Frank H. J. Schuren; Rita Verhelst; Tom Heskes (PSL, 2013)

Learning Sparse Causal Models is not NP-hard. - Tom Claassen; Joris M. Mooij; Tom Heskes (UAI, 2013)

Cyclic Causal Discovery from Continuous Equilibrium Data. - Joris M. Mooij; Tom Heskes (UAI, 2013)

Semi-supervised Ranking Pursuit. - Evgeni Tsivtsivadze; Tom Heskes (CoRR, 2013)

Efficiently learning the preferences of people. - Adriana Birlutiu; Perry Groot; Tom Heskes (Mach. Learn., 2013)

Bayesian Sparse Partial Least Squares. - Diego Vidaurre; Marcel A. J. van Gerven; Concha Bielza; Pedro Larrañaga; Tom Heskes (Neural Comput., 2013)

Bayesian inference of structural brain networks. - Max Hinne; Tom Heskes; Christian F. Beckmann; Marcel A. J. van Gerven (NeuroImage, 2013)

Linear reconstruction of perceived images from human brain activity. - Sanne Schoenmakers; Markus Barth; Tom Heskes; Marcel van Gerven (NeuroImage, 2013)

Unsupervised Feature Learning for Visual Sign Language Identification. - Binyam Gebrekidan Gebre; Onno Crasborn; Peter Wittenburg; Sebastian Drude; Tom Heskes (ACL (2), 2014)

Motion history images for online speaker/signer diarization. - Binyam Gebrekidan Gebre; Peter Wittenburg; Tom Heskes; Sebastian Drude (ICASSP, 2014)

Mutual Information Estimation with Random Forests. - Mike Koeman; Tom Heskes (ICONIP (2), 2014)

Speaker diarization using gesture and speech. - Binyam Gebrekidan Gebre; Peter Wittenburg; Sebastian Drude; Marijn Huijbregts; Tom Heskes (INTERSPEECH, 2014)

Causal Discovery from Databases with Discrete and Continuous Variables. - Elena Sokolova; Perry Groot; Tom Claassen; Tom Heskes (Probabilistic Graphical Models, 2014)

Using Topology Information for Protein-Protein Interaction Prediction. - Adriana Birlutiu; Tom Heskes (PRIB, 2014)

Gaussian mixture models improve fMRI-based image reconstruction. - Sanne Schoenmakers; Marcel van Gerven; Tom Heskes (PRNI, 2014)

A comparative study of cell classifiers for image-based high-throughput screening. - Syed Abbas; Tjeerd Dijkstra; Tom Heskes (BMC Bioinform., 2014)

A fast algorithm for determining bounds and accurate approximate p-values of the rank product statistic for replicate experiments. - Tom Heskes; Rob Eisinga; Rainer Breitling (BMC Bioinform., 2014)

Quantifying uncertainty in brain network measures using Bayesian connectomics. - Ronald J. Janssen; Max Hinne; Tom Heskes; Marcel A. J. van Gerven (Frontiers Comput. Neurosci., 2014)

Gaussian mixture models and semantic gating improve reconstructions from human brain activity. - Sanne Schoenmakers; Umut Güçlü; Marcel van Gerven; Tom Heskes (Frontiers Comput. Neurosci., 2014)

Premise Selection for Mathematics by Corpus Analysis and Kernel Methods. - Jesse Alama; Tom Heskes; Daniel Kühlwein; Evgeni Tsivtsivadze; Josef Urban (J. Autom. Reason., 2014)

Structurally-informed Bayesian functional connectivity analysis. - Max Hinne; Luca Ambrogioni; Ronald J. Janssen; Tom Heskes; Marcel A. J. van Gerven (NeuroImage, 2014)

Causal Discovery from Medical Data: Dealing with Missing Values and a Mixture of Discrete and Continuous Data. - Elena Sokolova; Perry Groot; Tom Claassen; Daniel von Rhein; Jan K. Buitelaar; Tom Heskes (AIME, 2015)

KeCo: Kernel-Based Online Co-agreement Algorithm. - Laurens Wiel; Tom Heskes; Evgeni Levin (Discovery Science, 2015)

Batch Steepest-Descent-Mildest-Ascent for Interactive Maximum Margin Clustering. - Fabian Gieseke; Tapio Pahikkala; Tom Heskes (IDA, 2015)

Domain Generalization Based on Transfer Component Analysis. - Thomas Grubinger; Adriana Birlutiu; Holger Schöner; Thomas Natschläger; Tom Heskes (IWANN (1), 2015)

Small white matter lesion detection in cerebral small vessel disease. - Mohsen Ghafoorian; Nico Karssemeijer; Inge van Uden; Frank-Erik de Leeuw; Tom Heskes; Elena Marchiori; Bram Platel (Computer-Aided Diagnosis, 2015)

Causality on Longitudinal Data: Stable Specification Search in Constrained Structural Equation Modeling. - Ridho Rahmadi; Perry Groot; Marianne Heins; Hans Knoop; Tom Heskes (AALTD@PKDD/ECML, 2015)

Hidden Markov Models for Reading Words from the Human Brain. - Sanne Schoenmakers; Tom Heskes; Marcel van Gerven (PRNI, 2015)

Bayesian Estimation of Conditional Independence Graphs Improves Functional Connectivity Estimates. - Max Hinne; Ronald J. Janssen; Tom Heskes; Marcel A. J. van Gerven (PLoS Comput. Biol., 2015)

MAGMA: Generalized Gene-Set Analysis of GWAS Data. - Christiaan A. de Leeuw; Joris M. Mooij; Tom Heskes; Danielle Posthuma (PLoS Comput. Biol., 2015)

A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions. - Adriana Birlutiu; Florence d'Alché-Buc; Tom Heskes (IEEE ACM Trans. Comput. Biol. Bioinform., 2015)

Exploring Constraint: Simulating Self-Organization and Autogenesis in the Autogenic Automaton. - Terrence W. Deacon; Tom Heskes; Stefan Leijnen (ALIFE, 2016)

Causal Discovery from Big Data - Mission (Im)possible?. - Tom Heskes (ICAART (1), 2016)

Non-uniform patch sampling with deep convolutional neural networks for white matter hyperintensity segmentation. - Mohsen Ghafoorian; Nico Karssemeijer; Tom Heskes; I. W. M. van Uden; Frank-Erik de Leeuw; Elena Marchiori; Bram van Ginneken; Bram Platel (ISBI, 2016)

A single-layer network unsupervised feature learning method for white matter hyperintensity segmentation. - Koen Vijverberg; Mohsen Ghafoorian; Inge W. M. van Uden; Frank-Erik de Leeuw; Bram Platel; Tom Heskes (Computer-Aided Diagnosis, 2016)

Computing Lower and Upper Bounds on the Probability of Causal Statements. - Elena Sokolova; Martine Hoogman; Perry Groot; Tom Claassen; Tom Heskes (Probabilistic Graphical Models, 2016)

Copula PC Algorithm for Causal Discovery from Mixed Data. - Ruifei Cui; Perry Groot; Tom Heskes (ECML/PKDD (2), 2016)

BCM: toolkit for Bayesian analysis of Computational Models using samplers. - Bram Thijssen; T. M. H. Dijkstra; Tom Heskes; Lodewyk F. A. Wessels (BMC Syst. Biol., 2016)

The Artificial Mind's Eye: Resisting Adversarials for Convolutional Neural Networks using Internal Projection. - Harm Berntsen; Wouter Kuijper; Tom Heskes (CoRR, 2016)

Deep Multi-scale Location-aware 3D Convolutional Neural Networks for Automated Detection of Lacunes of Presumed Vascular Origin. - 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 (CoRR, 2016)

Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities. - Mohsen Ghafoorian; Nico Karssemeijer; Tom Heskes; Inge van Uden; Clara I. Sánchez; Geert Litjens 001; Frank-Erik de Leeuw; Bram van Ginneken; Elena Marchiori; Bram Platel (CoRR, 2016)

Causality on Longitudinal Data: Stable Specification Search in Constrained Structural Equation Modeling. - Ridho Rahmadi; Perry Groot; Marieke M. H. C. van Rijn; Jan A. J. G. van den Brand; Marianne Heins; Hans Knoop; Tom Heskes (CoRR, 2016)

Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness. - Ioan Gabriel Bucur; Tom Claassen; Tom Heskes (AISTATS, 2017)

Robust Estimation of Gaussian Copula Causal Structure from Mixed Data with Missing Values. - Ruifei Cui; Perry Groot; Tom Heskes (ICDM, 2017)

Massively-parallel best subset selection for ordinary least-squares regression. - Fabian Gieseke; Kai Lars Polsterer; Ashish Mahabal; Christian Igel; Tom Heskes (SSCI, 2017)

Causality on cross-sectional data: Stable specification search in constrained structural equation modeling. - Ridho Rahmadi; Perry Groot; Marianne Heins; Hans Knoop; Tom Heskes (Appl. Soft Comput., 2017)

RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets. - Francesco Del Carratore; Andris Jankevics; Rob Eisinga; Tom Heskes; Fangxin Hong; Rainer Breitling (Bioinform., 2017)

Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers. - Rob Eisinga; Tom Heskes; Ben Pelzer; Manfred te Grotenhuis (BMC Bioinform., 2017)

Handling hybrid and missing data in constraint-based causal discovery to study the etiology of ADHD. - Elena Sokolova; Daniel von Rhein; Jilly Naaijen; Perry Groot; Tom Claassen; Jan K. Buitelaar; Tom Heskes (Int. J. Data Sci. Anal., 2017)

Multi-Domain Transfer Component Analysis for Domain Generalization. - Thomas Grubinger; Adriana Birlutiu; Holger Schöner; Thomas Natschläger; Tom Heskes (Neural Process. Lett., 2017)

Expectation Propagation. - Tom Heskes (Encyclopedia of Machine Learning and Data Mining, 2017)

A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks. - Ioan Gabriel Bucur; Tom van Bussel; Tom Claassen; Tom Heskes (PGM, 2018)

Learning the Causal Structure of Copula Models with Latent Variables. - Ruifei Cui; Perry Groot; Moritz Schauer; Tom Heskes (UAI, 2018)

Bigger Buffer k-d Trees on Multi-Many-Core Systems. - Fabian Gieseke; Cosmin Eugen Oancea; Ashish Mahabal; Christian Igel; Tom Heskes (VECPAR, 2018)

Bayesian data integration for quantifying the contribution of diverse measurements to parameter estimates. - Bram Thijssen; Tjeerd Dijkstra; Tom Heskes; Lodewyk F. A. Wessels (Bioinform., 2018)

Stable specification search in structural equation model with latent variables. - Ridho Rahmadi; Perry Groot; Tom Heskes (CoRR, 2018)

The stablespec package for causal discovery on cross-sectional and longitudinal data in R. - Ridho Rahmadi; Perry Groot; Tom Heskes (Neurocomputing, 2018)

A scalable preference model for autonomous decision-making. - Markus Peters; Maytal Saar-Tsechansky; Wolfgang Ketter; Sinead A. Williamson; Perry Groot; Tom Heskes (Mach. Learn., 2018)

Constraining the Parameters of High-Dimensional Models with Active Learning. - Sascha Caron; Tom Heskes; Sydney Otten; Bob Stienen (CoRR, 2019)

Large-scale local causal inference of gene regulatory relationships. - Ioan Gabriel Bucur; Tom Claassen; Tom Heskes (Int. J. Approx. Reason., 2019)

Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies. - Payam Piray; Amir Dezfouli; Tom Heskes; Michael J. Frank; Nathaniel D. Daw (PLoS Comput. Biol., 2019)

A novel Bayesian approach for latent variable modeling from mixed data with missing values. - Ruifei Cui; Ioan Gabriel Bucur; Perry Groot; Tom Heskes (Stat. Comput., 2019)

Learning causal structure from mixed data with missing values using Gaussian copula models. - Ruifei Cui; Perry Groot; Tom Heskes (Stat. Comput., 2019)

Stable Specification Search in Structural Equation Models with Latent Variables. - Ridho Rahmadi; Perry Groot; Tom Heskes (ACM Trans. Intell. Syst. Technol., 2019)

Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models. - Tom Heskes; Evi Sijben; Ioan Gabriel Bucur; Tom Claassen (NeurIPS, 2020)

Discovering cause-effect relationships in spatial systems with a known direction based on observational data. - Konrad P. Mielke; Tom Claassen; Mark A. J. Huijbregts; Aafke M. Schipper; Tom M. Heskes (PGM, 2020)

MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models. - Ioan Gabriel Bucur; Tom Claassen; Tom Heskes (UAI, 2020)

Learning Equational Theorem Proving. - Jelle Piepenbrock; Tom Heskes; Mikolás Janota; Josef Urban (CoRR, 2021)

Swift sky localization of gravitational waves using deep learning seeded importance sampling. - Alex Kolmus; Grégory Baltus; Justin Janquart; Twan van Laarhoven; Sarah Caudill; Tom Heskes (CoRR, 2021)

Going Grayscale: The Road to Understanding and Improving Unlearnable Examples. - Zhuoran Liu 001; Zhengyu Zhao 001; Alex Kolmus; Tijn Berns; Twan van Laarhoven; Tom Heskes; Martha A. Larson (CoRR, 2021)

Spectral Ranking of Causal Influence in Complex Systems. - Errol Zalmijn; Tom Heskes; Tom Claassen (Entropy, 2021)

Probabilistic Modelling of Gait for Robust Passive Monitoring in Daily Life. - Yordan P. Raykov; Luc J. W. Evers; Reham Badawy; Bastiaan R. Bloem; Tom M. Heskes; Marjan J. Meinders; Kasper Claes; Max A. Little (IEEE J. Biomed. Health Informatics, 2021)

Guiding an Automated Theorem Prover with Neural Rewriting. - Jelle Piepenbrock; Tom Heskes; Mikolás Janota; Josef Urban (IJCAR, 2022)

Non-parametric synergy modeling of chemical compounds with Gaussian processes. - Yuliya Shapovalova; Tom Heskes; Tjeerd Dijkstra (BMC Bioinform., 2022)

Confident Neural Network Regression with Bootstrapped Deep Ensembles. - Laurens Sluijterman; Eric Cator; Tom Heskes (CoRR, 2022)

Machine Learning Meets The Herbrand Universe. - Jelle Piepenbrock; Josef Urban; Konstantin Korovin; Miroslav Olsák; Tom Heskes; Mikolas Janota (CoRR, 2022)

Beyond the Markov Equivalence Class: Extending Causal Discovery under Latent Confounding. - Mirthe Maria Van Diepen; Ioan Gabriel Bucur; Tom Heskes; Tom Claassen (CLeaR, 2023)

Graph Isomorphic Networks for Assessing Reliability of the Medium-Voltage Grid. - Charlotte Cambier van Nooten; Tom van de Poll; Sonja Füllhase; Jacco Heres; Tom Heskes; Yuliya Shapovalova (CoRR, 2023)

Fault Trees, Decision Trees, And Binary Decision Diagrams: A Systematic Comparison. - Lisandro Arturo Jimenez-Roa; Tom Heskes; Mariëlle Stoelinga (CoRR, 2023)

Automatic Inference of Fault Tree Models Via Multi-Objective Evolutionary Algorithms. - Lisandro Arturo Jimenez-Roa; Tom Heskes; Tiedo Tinga; Mariëlle Stoelinga (IEEE Trans. Dependable Secur. Comput., 2023)

AutoCD: Automated Machine Learning for Causal Discovery Algorithms. - Gerlise Chan; Tom Claassen; Holger H. Hoos; Tom Heskes; Mitra Baratchi (PGM, 2024)

Pfeed: Generating near real-time personalized feeds using precomputed embedding similarities. - Binyam Gebre; Karoliina Ranta; Stef van den Elzen; Ernst Kuiper; Thijs Baars; Tom Heskes (CoRR, 2024)

Acquiring Better Load Estimates by Combining Anomaly and Change-point Detection in Power Grid Time-series Measurements. - Roel Bouman; Linda Schmeitz; Luco Buise; Jacco Heres; Yuliya Shapovalova; Tom Heskes (CoRR, 2024)

Composite Quantile Regression With XGBoost Using the Novel Arctan Pinball Loss. - Laurens Sluijterman; Frank Kreuwel; Eric Cator; Tom Heskes (CoRR, 2024)

Optimal training of Mean Variance Estimation neural networks. - Laurens Sluijterman; Eric Cator; Tom Heskes (Neurocomputing, 2024)

Unsupervised Anomaly Detection Algorithms on Real-world Data: How Many Do We Need? - Roel Bouman; Zaharah Bukhsh; Tom Heskes (J. Mach. Learn. Res., 2024)

How to evaluate uncertainty estimates in machine learning for regression? - Laurens Sluijterman; Eric Cator; Tom Heskes (Neural Networks, 2024)

Offline Changepoint Detection With Gaussian Processes. - Janneke Verbeek; Tom Heskes; Yuliya Shapovalova (UAI, 2025)

Invariant neural architecture for learning term synthesis in instantiation proving. - Jelle Piepenbrock; Josef Urban; Konstantin Korovin; Miroslav Olsák; Tom Heskes; Mikolás Janota (J. Symb. Comput., 2025)

Autoencoders for Anomaly Detection are Unreliable. - Roel Bouman; Tom Heskes (CoRR, 2025)

Bias-variance decompositions: the exclusive privilege of Bregman divergences. - Tom Heskes (CoRR, 2025)

Likelihood-ratio-based confidence intervals for neural networks. - Laurens Sluijterman; Eric Cator; Tom Heskes (Mach. Learn., 2025)