Tom Heskes - RankMe

219 papers A* 5A 21B 13C 4Misc 2Journal 116Unranked 54

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	Autoencoders for Anomaly Detection are Unreliable. CoRR Roel Bouman, Tom Heskes
2025	J	`jnl`	Bias-variance decompositions: the exclusive privilege of Bregman divergences. CoRR Tom Heskes
2025	J	`jnl`	Bias-variance decompositions: the exclusive privilege of Bregman divergences. J. Mach. Learn. Res. Tom Heskes
2025	J	`jnl`	Composite quantile regression with XGBoost using the novel arctan pinball loss. Int. J. Mach. Learn. Cybern. Laurens Sluijterman, Frank Kreuwel, Eric Cator, Tom Heskes
2025	J	`jnl`	Confident neural network regression with Bootstrapped Deep Ensembles. Neurocomputing Laurens Sluijterman, Eric Cator, Tom Heskes
2025	J	`jnl`	Invariant neural architecture for learning term synthesis in instantiation proving. J. Symb. Comput. Jelle Piepenbrock, Josef Urban, Konstantin Korovin, Miroslav Olsák, Tom Heskes, Mikolás Janota
2025	J	`jnl`	Likelihood-ratio-based confidence intervals for neural networks. Mach. Learn. Laurens Sluijterman, Eric Cator, Tom Heskes
2025	A	`conf`	Offline Changepoint Detection With Gaussian Processes. UAI Janneke Verbeek, Tom Heskes, Yuliya Shapovalova
2024	J	`jnl`	Acquiring Better Load Estimates by Combining Anomaly and Change-point Detection in Power Grid Time-series Measurements. CoRR Roel Bouman, Linda Schmeitz, Luco Buise, Jacco Heres, Yuliya Shapovalova, Tom Heskes
2024	—	`conf`	AutoCD: Automated Machine Learning for Causal Discovery Algorithms. PGM Gerlise Chan, Tom Claassen, Holger H. Hoos, Tom Heskes, Mitra Baratchi
2024	J	`jnl`	Composite Quantile Regression With XGBoost Using the Novel Arctan Pinball Loss. CoRR Laurens Sluijterman, Frank Kreuwel, Eric Cator, Tom Heskes
2024	J	`jnl`	How to evaluate uncertainty estimates in machine learning for regression? Neural Networks Laurens Sluijterman, Eric Cator, Tom Heskes
2024	J	`jnl`	Optimal training of Mean Variance Estimation neural networks. Neurocomputing Laurens Sluijterman, Eric Cator, Tom Heskes
2024	J	`jnl`	Pfeed: Generating near real-time personalized feeds using precomputed embedding similarities. CoRR Binyam Gebre, Karoliina Ranta, Stef van den Elzen, Ernst Kuiper, Thijs Baars, Tom Heskes
2024	J	`jnl`	Unsupervised Anomaly Detection Algorithms on Real-world Data: How Many Do We Need? J. Mach. Learn. Res. Roel Bouman, Zaharah Bukhsh, Tom Heskes
2023	J	`jnl`	Automatic Inference of Fault Tree Models Via Multi-Objective Evolutionary Algorithms. IEEE Trans. Dependable Secur. Comput. Lisandro Arturo Jimenez-Roa, Tom Heskes, Tiedo Tinga, Mariëlle Stoelinga
2023	—	`conf`	Beyond the Markov Equivalence Class: Extending Causal Discovery under Latent Confounding. CLeaR Mirthe Maria Van Diepen, Ioan Gabriel Bucur, Tom Heskes, Tom Claassen
2023	J	`jnl`	Fault Trees, Decision Trees, And Binary Decision Diagrams: A Systematic Comparison. CoRR Lisandro Arturo Jimenez-Roa, Tom Heskes, Mariëlle Stoelinga
2023	J	`jnl`	Graph Isomorphic Networks for Assessing Reliability of the Medium-Voltage Grid. CoRR Charlotte Cambier van Nooten, Tom van de Poll, Sonja Füllhase, Jacco Heres, Tom Heskes, Yuliya Shapovalova
2023	J	`jnl`	Likelihood-ratio-based confidence intervals for neural networks. CoRR Laurens Sluijterman, Eric Cator, Tom Heskes
2023	J	`jnl`	Optimal Training of Mean Variance Estimation Neural Networks. CoRR Laurens Sluijterman, Eric Cator, Tom Heskes
2023	J	`jnl`	Unsupervised anomaly detection algorithms on real-world data: how many do we need? CoRR Roel Bouman, Zaharah Bukhsh, Tom Heskes
2022	J	`jnl`	Automatic inference of fault tree models via multi-objective evolutionary algorithms. CoRR Lisandro Arturo Jimenez-Roa, Tom Heskes, Tiedo Tinga, Mariëlle Stoelinga
2022	J	`jnl`	Confident Neural Network Regression with Bootstrapped Deep Ensembles. CoRR Laurens Sluijterman, Eric Cator, Tom Heskes
2022	A	`conf`	Guiding an Automated Theorem Prover with Neural Rewriting. IJCAR Jelle Piepenbrock, Tom Heskes, Mikolás Janota, Josef Urban
2022	J	`jnl`	Machine Learning Meets The Herbrand Universe. CoRR Jelle Piepenbrock, Josef Urban, Konstantin Korovin, Miroslav Olsák, Tom Heskes, Mikolas Janota
2022	J	`jnl`	Non-parametric synergy modeling of chemical compounds with Gaussian processes. BMC Bioinform. Yuliya Shapovalova, Tom Heskes, Tjeerd Dijkstra
2021	J	`jnl`	Going Grayscale: The Road to Understanding and Improving Unlearnable Examples. CoRR Zhuoran Liu, Zhengyu Zhao, Alex Kolmus, Tijn Berns, Twan van Laarhoven, Tom Heskes, Martha A. Larson
2021	J	`jnl`	How to Evaluate Uncertainty Estimates in Machine Learning for Regression? CoRR Laurens Sluijterman, Eric Cator, Tom Heskes
2021	J	`jnl`	Learning Equational Theorem Proving. CoRR Jelle Piepenbrock, Tom Heskes, Mikolás Janota, Josef Urban
2021	J	`jnl`	Spectral Ranking of Causal Influence in Complex Systems. Entropy Errol Zalmijn, Tom Heskes, Tom Claassen
2021	J	`jnl`	Swift sky localization of gravitational waves using deep learning seeded importance sampling. CoRR Alex Kolmus, Grégory Baltus, Justin Janquart, Twan van Laarhoven, Sarah Caudill, Tom Heskes
2020	A*	`conf`	Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models. NeurIPS Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen
2020	J	`jnl`	Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models. CoRR Tom Heskes, Evi Sijben, Ioan Gabriel Bucur, Tom Claassen
2020	A	`conf`	MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models. UAI Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
2020	J	`jnl`	MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models. CoRR Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
2020	J	`jnl`	Spectral Ranking of Causal Influence in Complex Systems. CoRR Errol Zalmijn, Tom Heskes, Tom Claassen
2019	J	`jnl`	A novel Bayesian approach for latent variable modeling from mixed data with missing values. Stat. Comput. Ruifei Cui, Ioan Gabriel Bucur, Perry Groot, Tom Heskes
2019	J	`jnl`	Constraining the Parameters of High-Dimensional Models with Active Learning. CoRR Sascha Caron, Tom Heskes, Sydney Otten, Bob Stienen
2019	J	`jnl`	Hierarchical Bayesian inference for concurrent model fitting and comparison for group studies. PLoS Comput. Biol. Payam Piray, Amir Dezfouli, Tom Heskes, Michael J. Frank, Nathaniel D. Daw
2019	J	`jnl`	Large-Scale Local Causal Inference of Gene Regulatory Relationships. CoRR Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
2019	J	`jnl`	Large-scale local causal inference of gene regulatory relationships. Int. J. Approx. Reason. Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
2019	J	`jnl`	Learning causal structure from mixed data with missing values using Gaussian copula models. Stat. Comput. Ruifei Cui, Perry Groot, Tom Heskes
2019	J	`jnl`	Stable Specification Search in Structural Equation Models with Latent Variables. ACM Trans. Intell. Syst. Technol. Ridho Rahmadi, Perry Groot, Tom Heskes
2018	—	`conf`	A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks. PGM Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen, Tom Heskes
2018	J	`jnl`	A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks. CoRR Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen, Tom Heskes
2018	J	`jnl`	A Novel Bayesian Approach for Latent Variable Modeling from Mixed Data with Missing Values. CoRR Ruifei Cui, Ioan Gabriel Bucur, Perry Groot, Tom Heskes
2018	J	`jnl`	A scalable preference model for autonomous decision-making. Mach. Learn. Markus Peters, Maytal Saar-Tsechansky, Wolfgang Ketter, Sinead A. Williamson, Perry Groot, Tom Heskes
2018	J	`jnl`	Bayesian data integration for quantifying the contribution of diverse measurements to parameter estimates. Bioinform. Bram Thijssen, Tjeerd Dijkstra, Tom Heskes, Lodewyk F. A. Wessels
2018	—	`conf`	Bigger Buffer k-d Trees on Multi-Many-Core Systems. VECPAR Fabian Gieseke, Cosmin Eugen Oancea, Ashish Mahabal, Christian Igel, Tom Heskes
2018	A	`conf`	Learning the Causal Structure of Copula Models with Latent Variables. UAI Ruifei Cui, Perry Groot, Moritz Schauer, Tom Heskes
2018	J	`jnl`	Stable specification search in structural equation model with latent variables. CoRR Ridho Rahmadi, Perry Groot, Tom Heskes
2018	J	`jnl`	The stablespec package for causal discovery on cross-sectional and longitudinal data in R. Neurocomputing Ridho Rahmadi, Perry Groot, Tom Heskes
2017	J	`jnl`	Causality on cross-sectional data: Stable specification search in constrained structural equation modeling. Appl. Soft Comput. Ridho Rahmadi, Perry Groot, Marianne Heins, Hans Knoop, Tom Heskes
2017	J	`jnl`	Exact p-values for pairwise comparison of Friedman rank sums, with application to comparing classifiers. BMC Bioinform. Rob Eisinga, Tom Heskes, Ben Pelzer, Manfred te Grotenhuis
2017	—	`ch.`	Expectation Propagation. Encyclopedia of Machine Learning and Data Mining Tom Heskes
2017	J	`jnl`	Handling hybrid and missing data in constraint-based causal discovery to study the etiology of ADHD. Int. J. Data Sci. Anal. Elena Sokolova, Daniel von Rhein, Jilly Naaijen, Perry Groot, Tom Claassen, Jan K. Buitelaar, Tom Heskes
2017	—	`conf`	Massively-parallel best subset selection for ordinary least-squares regression. SSCI Fabian Gieseke, Kai Lars Polsterer, Ashish Mahabal, Christian Igel, Tom Heskes
2017	J	`jnl`	Multi-Domain Transfer Component Analysis for Domain Generalization. Neural Process. Lett. Thomas Grubinger, Adriana Birlutiu, Holger Schöner, Thomas Natschläger, Tom Heskes
2017	J	`jnl`	RankProd 2.0: a refactored bioconductor package for detecting differentially expressed features in molecular profiling datasets. Bioinform. Francesco Del Carratore, Andris Jankevics, Rob Eisinga, Tom Heskes, Fangxin Hong, Rainer Breitling
2017	A	`conf`	Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness. AISTATS Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
2017	J	`jnl`	Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness. CoRR Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
2017	A*	`conf`	Robust Estimation of Gaussian Copula Causal Structure from Mixed Data with Missing Values. ICDM Ruifei Cui, Perry Groot, Tom Heskes
2016	—	`conf`	A single-layer network unsupervised feature learning method for white matter hyperintensity segmentation. Computer-Aided Diagnosis Koen Vijverberg, Mohsen Ghafoorian, Inge W. M. van Uden, Frank-Erik de Leeuw, Bram Platel, Tom Heskes
2016	J	`jnl`	BCM: toolkit for Bayesian analysis of Computational Models using samplers. BMC Syst. Biol. Bram Thijssen, T. M. H. Dijkstra, Tom Heskes, Lodewyk F. A. Wessels
2016	—	`conf`	Causal Discovery from Big Data - Mission (Im)possible?. ICAART (1) Tom Heskes
2016	J	`jnl`	Causality on Longitudinal Data: Stable Specification Search in Constrained Structural Equation Modeling. CoRR Ridho Rahmadi, Perry Groot, Marieke M. H. C. van Rijn, Jan A. J. G. van den Brand, Marianne Heins, Hans Knoop, Tom Heskes
2016	—	`conf`	Computing Lower and Upper Bounds on the Probability of Causal Statements. Probabilistic Graphical Models Elena Sokolova, Martine Hoogman, Perry Groot, Tom Claassen, Tom Heskes
2016	—	`conf`	Copula PC Algorithm for Causal Discovery from Mixed Data. ECML/PKDD (2) Ruifei Cui, Perry Groot, Tom Heskes
2016	J	`jnl`	Deep Multi-scale Location-aware 3D Convolutional Neural Networks for Automated Detection of Lacunes of Presumed Vascular Origin. CoRR 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
2016	C	`conf`	Exploring Constraint: Simulating Self-Organization and Autogenesis in the Autogenic Automaton. ALIFE Terrence W. Deacon, Tom Heskes, Stefan Leijnen
2016	J	`jnl`	Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities. CoRR Mohsen Ghafoorian, Nico Karssemeijer, Tom Heskes, Inge van Uden, Clara I. Sánchez, Geert Litjens, Frank-Erik de Leeuw, Bram van Ginneken, Elena Marchiori, Bram Platel
2016	—	`conf`	Non-uniform patch sampling with deep convolutional neural networks for white matter hyperintensity segmentation. ISBI Mohsen Ghafoorian, Nico Karssemeijer, Tom Heskes, I. W. M. van Uden, Frank-Erik de Leeuw, Elena Marchiori, Bram van Ginneken, Bram Platel
2016	J	`jnl`	The Artificial Mind's Eye: Resisting Adversarials for Convolutional Neural Networks using Internal Projection. CoRR Harm Berntsen, Wouter Kuijper, Tom Heskes
2015	J	`jnl`	A Bayesian Framework for Combining Protein and Network Topology Information for Predicting Protein-Protein Interactions. IEEE ACM Trans. Comput. Biol. Bioinform. Adriana Birlutiu, Florence d'Alché-Buc, Tom Heskes
2015	B	`conf`	Batch Steepest-Descent-Mildest-Ascent for Interactive Maximum Margin Clustering. IDA Fabian Gieseke, Tapio Pahikkala, Tom Heskes
2015	J	`jnl`	Bayesian Estimation of Conditional Independence Graphs Improves Functional Connectivity Estimates. PLoS Comput. Biol. Max Hinne, Ronald J. Janssen, Tom Heskes, Marcel A. J. van Gerven
2015	J	`jnl`	Bigger Buffer k-d Trees on Multi-Many-Core Systems. CoRR Fabian Gieseke, Cosmin Eugen Oancea, Ashish Mahabal, Christian Igel, Tom Heskes
2015	B	`conf`	Causal Discovery from Medical Data: Dealing with Missing Values and a Mixture of Discrete and Continuous Data. AIME Elena Sokolova, Perry Groot, Tom Claassen, Daniel von Rhein, Jan K. Buitelaar, Tom Heskes
2015	J	`jnl`	Causality on Cross-Sectional Data: Stable Specification Search in Constrained Structural Equation Modeling. CoRR Ridho Rahmadi, Perry Groot, Marianne Heins, Hans Knoop, Tom Heskes
2015	—	`conf`	Causality on Longitudinal Data: Stable Specification Search in Constrained Structural Equation Modeling. AALTD@PKDD/ECML Ridho Rahmadi, Perry Groot, Marianne Heins, Hans Knoop, Tom Heskes
2015	—	`conf`	Domain Generalization Based on Transfer Component Analysis. IWANN (1) Thomas Grubinger, Adriana Birlutiu, Holger Schöner, Thomas Natschläger, Tom Heskes
2015	—	`conf`	Hidden Markov Models for Reading Words from the Human Brain. PRNI Sanne Schoenmakers, Tom Heskes, Marcel van Gerven
2015	B	`conf`	KeCo: Kernel-Based Online Co-agreement Algorithm. Discovery Science Laurens Wiel, Tom Heskes, Evgeni Levin
2015	J	`jnl`	MAGMA: Generalized Gene-Set Analysis of GWAS Data. PLoS Comput. Biol. Christiaan A. de Leeuw, Joris M. Mooij, Tom Heskes, Danielle Posthuma
2015	A	`ed.`	Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, UAI 2015, July 12-16, 2015, Amsterdam, The Netherlands UAI Marina Meila, Tom Heskes
2015	—	`conf`	Small white matter lesion detection in cerebral small vessel disease. Computer-Aided Diagnosis Mohsen Ghafoorian, Nico Karssemeijer, Inge van Uden, Frank-Erik de Leeuw, Tom Heskes, Elena Marchiori, Bram Platel
2014	J	`jnl`	A comparative study of cell classifiers for image-based high-throughput screening. BMC Bioinform. Syed Abbas, Tjeerd Dijkstra, Tom Heskes
2014	J	`jnl`	A fast algorithm for determining bounds and accurate approximate p-values of the rank product statistic for replicate experiments. BMC Bioinform. Tom Heskes, Rob Eisinga, Rainer Breitling
2014	—	`conf`	Causal Discovery from Databases with Discrete and Continuous Variables. Probabilistic Graphical Models Elena Sokolova, Perry Groot, Tom Claassen, Tom Heskes
2014	J	`jnl`	Gaussian mixture models and semantic gating improve reconstructions from human brain activity. Frontiers Comput. Neurosci. Sanne Schoenmakers, Umut Güçlü, Marcel van Gerven, Tom Heskes
2014	—	`conf`	Gaussian mixture models improve fMRI-based image reconstruction. PRNI Sanne Schoenmakers, Marcel van Gerven, Tom Heskes
2014	Misc	`conf`	Motion history images for online speaker/signer diarization. ICASSP Binyam Gebrekidan Gebre, Peter Wittenburg, Tom Heskes, Sebastian Drude
2014	—	`conf`	Mutual Information Estimation with Random Forests. ICONIP (2) Mike Koeman, Tom Heskes
2014	J	`jnl`	Premise Selection for Mathematics by Corpus Analysis and Kernel Methods. J. Autom. Reason. Jesse Alama, Tom Heskes, Daniel Kühlwein, Evgeni Tsivtsivadze, Josef Urban
2014	J	`jnl`	Properties of Bethe Free Energies and Message Passing in Gaussian Models. CoRR Botond Cseke, Tom Heskes
2014	J	`jnl`	Quantifying uncertainty in brain network measures using Bayesian connectomics. Frontiers Comput. Neurosci. Ronald J. Janssen, Max Hinne, Tom Heskes, Marcel A. J. van Gerven
2014	A	`conf`	Speaker diarization using gesture and speech. INTERSPEECH Binyam Gebrekidan Gebre, Peter Wittenburg, Sebastian Drude, Marijn Huijbregts, Tom Heskes
2014	J	`jnl`	Structurally-informed Bayesian functional connectivity analysis. NeuroImage Max Hinne, Luca Ambrogioni, Ronald J. Janssen, Tom Heskes, Marcel A. J. van Gerven
2014	—	`conf`	Unsupervised Feature Learning for Visual Sign Language Identification. ACL (2) Binyam Gebrekidan Gebre, Onno Crasborn, Peter Wittenburg, Sebastian Drude, Tom Heskes
2014	—	`conf`	Using Topology Information for Protein-Protein Interaction Prediction. PRIB Adriana Birlutiu, Tom Heskes
2013	—	`conf`	Automatic Signer Diarization - The Mover Is the Signer Approach. CVPR Workshops Binyam Gebrekidan Gebre, Peter Wittenburg, Tom Heskes
2013	B	`conf`	Automatic sign language identification. ICIP Binyam Gebrekidan Gebre, Peter Wittenburg, Tom Heskes
2013	A*	`conf`	Bayesian Probabilities for Constraint-Based Causal Discovery. IJCAI Tom Claassen, Tom Heskes
2013	J	`jnl`	Bayesian Sparse Partial Least Squares. Neural Comput. Diego Vidaurre, Marcel A. J. van Gerven, Concha Bielza, Pedro Larrañaga, Tom Heskes
2013	J	`jnl`	Bayesian inference of structural brain networks. NeuroImage Max Hinne, Tom Heskes, Christian F. Beckmann, Marcel A. J. van Gerven
2013	A	`conf`	Cyclic Causal Discovery from Continuous Equilibrium Data. UAI Joris M. Mooij, Tom Heskes
2013	J	`jnl`	Cyclic Causal Discovery from Continuous Equilibrium Data. CoRR Joris M. Mooij, Tom Heskes
2013	J	`jnl`	Efficiently learning the preferences of people. Mach. Learn. Adriana Birlutiu, Perry Groot, Tom Heskes
2013	J	`jnl`	Expectation Propogation for approximate inference in dynamic Bayesian networks CoRR Tom Heskes, Onno Zoeter
2013	J	`jnl`	IPF for Discrete Chain Factor Graphs CoRR Wim Wiegerinck, Tom Heskes
2013	—	`conf`	Improving Native Language Identification with TF-IDF Weighting. BEA@NAACL-HLT Binyam Gebrekidan Gebre, Marcos Zampieri, Peter Wittenburg, Tom Heskes
2013	A	`conf`	Learning Sparse Causal Models is not NP-hard. UAI Tom Claassen, Joris M. Mooij, Tom Heskes
2013	J	`jnl`	Learning Sparse Causal Models is not NP-hard. CoRR Tom Claassen, Joris M. Mooij, Tom Heskes
2013	J	`jnl`	Linear reconstruction of perceived images from human brain activity. NeuroImage Sanne Schoenmakers, Markus Barth, Tom Heskes, Marcel van Gerven
2013	—	`conf`	Multi-view Multi-class Classification for Identification of Pathogenic Bacterial Strains. MCS Evgeni Tsivtsivadze, Tom Heskes, Armand Paauw
2013	—	`conf`	Neighborhood Co-regularized Multi-view Spectral Clustering of Microbiome Data. PSL Evgeni Tsivtsivadze, Hanneke Borgdorff, Janneke van de Wijgert, Frank H. J. Schuren, Rita Verhelst, Tom Heskes
2013	J	`jnl`	Semi-supervised Ranking Pursuit. CoRR Evgeni Tsivtsivadze, Tom Heskes
2013	Misc	`conf`	The gesturer is the speaker. ICASSP Binyam Gebrekidan Gebre, Peter Wittenburg, Tom Heskes
2012	J	`jnl`	A Bayesian Approach to Constraint Based Causal Inference CoRR Tom Claassen, Tom Heskes
2012	A	`conf`	A Bayesian Approach to Constraint Based Causal Inference. UAI Tom Claassen, Tom Heskes
2012	—	`conf`	A Linear Gaussian Framework for Decoding of Perceived Images. PRNI Marcel A. J. van Gerven, Tom Heskes
2012	J	`jnl`	A Logical Characterization of Constraint-Based Causal Discovery CoRR Tom Claassen, Tom Heskes
2012	J	`jnl`	Approximate Inference and Constrained Optimization CoRR Tom Heskes, Kees Albers, Hilbert J. Kappen
2012	J	`jnl`	Bounds on the Bethe Free Energy for Gaussian Networks CoRR Botond Cseke, Tom Heskes
2012	J	`jnl`	Molecular Machines in the Synapse: Overlapping Protein Sets Control Distinct Steps in Neurosecretion. PLoS Comput. Biol. L. Niels Cornelisse, Evgeni Tsivtsivadze, Marieke Meijer, Tjeerd Dijkstra, Tom Heskes, Matthijs Verhage
2012	B	`conf`	Online Co-regularized Algorithms. Discovery Science Tom de Ruijter, Evgeni Tsivtsivadze, Tom Heskes
2012	A	`conf`	Overview and Evaluation of Premise Selection Techniques for Large Theory Mathematics. IJCAR Daniel Kühlwein, Twan van Laarhoven, Evgeni Tsivtsivadze, Josef Urban, Tom Heskes
2011	A	`conf`	A Logical Characterization of Constraint-Based Causal Discovery. UAI Tom Claassen, Tom Heskes
2011	—	`conf`	A Markov Random Field Approach to Neural Encoding and Decoding. ICANN (2) Marcel van Gerven, Eric Maris, Tom Heskes
2011	B	`conf`	A structure independent algorithm for causal discovery. ESANN Tom Claassen, Tom Heskes
2011	J	`jnl`	Approximate Marginals in Latent Gaussian Models. J. Mach. Learn. Res. Botond Cseke, Tom Heskes
2011	—	`conf`	Covert Attention as a Paradigm for Subject-Independent Brain-Computer Interfacing. MLINI Hans J. P. Wouters, Marcel A. J. van Gerven, Matthias Sebastian Treder, Tom Heskes, Ali Bahramisharif
2011	J	`jnl`	Dynamic decoding of ongoing perception. NeuroImage Marcel van Gerven, Peter Kok, Floris P. de Lange, Tom Heskes
2011	J	`jnl`	Editorial: One Year as EiC, and Editorial-Board Changes at TNN. IEEE Trans. Neural Networks 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, Michael G. Paulin, Robi Polikar, Danil V. Prokhorov, Marco A. Wiering, Vicente Zarzoso
2011	—	`conf`	Learning from Multiple Annotators with Gaussian Processes. ICANN (2) Perry Groot, Adriana Birlutiu, Tom Heskes
2011	B	`conf`	Learning of causal relations. ESANN John A. Quinn, Joris M. Mooij, Tom Heskes, Michael Biehl
2011	—	`conf`	Learning2Reason. Calculemus/MKM Daniel Kühlwein, Josef Urban, Evgeni Tsivtsivadze, Herman Geuvers, Tom Heskes
2011	—	`conf`	Multi-output Ranking for Automated Reasoning. KDIR Daniel Kühlwein, Josef Urban, Evgeni Tsivtsivadze, Herman Geuvers, Tom Heskes
2011	—	`conf`	On Causal Discovery with Cyclic Additive Noise Models. NIPS Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf
2011	J	`jnl`	Predicting Preference Judgments of Individual Normal and Hearing-Impaired Listeners With Gaussian Processes. IEEE Trans. Speech Audio Process. Perry Groot, Tom Heskes, Tjeerd Dijkstra, James M. Kates
2011	J	`jnl`	Premise Selection for Mathematics by Corpus Analysis and Kernel Methods CoRR Jesse Alama, Daniel Kühlwein, Evgeni Tsivtsivadze, Josef Urban, Tom Heskes
2011	J	`jnl`	Properties of Bethe Free Energies and Message Passing in Gaussian Models. J. Artif. Intell. Res. Botond Cseke, Tom Heskes
2011	A	`conf`	Semantic Graph Kernels for Automated Reasoning. SDM Evgeni Tsivtsivadze, Josef Urban, Herman Geuvers, Tom Heskes
2011	—	`conf`	The Dynamic Beamformer. MLINI Ali Bahramisharif, Marcel A. J. van Gerven, Jan-Mathijs Schoffelen, Zoubin Ghahramani, Tom Heskes
2010	A	`conf`	Bayesian Monte Carlo for the Global Optimization of Expensive Functions. ECAI Perry Groot, Adriana Birlutiu, Tom Heskes
2010	—	`conf`	Causal discovery in multiple models from different experiments. NIPS Tom Claassen, Tom Heskes
2010	—	`ch.`	Co-Regularized Least-Squares for Label Ranking. Preference Learning Evgeni Tsivtsivadze, Tapio Pahikkala, Jorma Boberg, Tapio Salakoski, Tom Heskes
2010	J	`jnl`	Efficient Bayesian multivariate fMRI analysis using a sparsifying spatio-temporal prior. NeuroImage Marcel A. J. van Gerven, Botond Cseke, Floris P. de Lange, Tom Heskes
2010	—	`ch.`	Expectation Propagation. Encyclopedia of Machine Learning Tom Heskes
2010	A	`conf`	Improving posterior marginal approximations in latent Gaussian models. AISTATS Botond Cseke, Tom Heskes
2010	J	`jnl`	Multi-task preference learning with an application to hearing aid personalization. Neurocomputing Adriana Birlutiu, Perry Groot, Tom Heskes
2010	J	`jnl`	Neural Decoding with Hierarchical Generative Models. Neural Comput. Marcel van Gerven, Floris P. de Lange, Tom Heskes
2010	—	`ed.`	Pattern Recognition in Bioinformatics - 5th IAPR International Conference, PRIB 2010, Nijmegen, The Netherlands, September 22-24, 2010. Proceedings PRIB Tjeerd Dijkstra, Evgeni Tsivtsivadze, Elena Marchiori, Tom Heskes
2009	—	`conf`	Bayesian Source Localization with the Multivariate Laplace Prior. NIPS Marcel van Gerven, Botond Cseke, Robert Oostenveld, Tom Heskes
2009	B	`conf`	Exploring the impact of alternative feature representations on BCI classification. ESANN Ali Bahramisharif, Marcel van Gerven, Tom Heskes
2009	J	`jnl`	Gene regulation in the intraerythrocytic cycle of Bioinform. Rasa Jurgelenaite, Tjeerd Dijkstra, Clemens H. M. Kocken, Tom Heskes
2009	J	`jnl`	Interpreting single trial data using groupwise regularisation. NeuroImage Marcel van Gerven, Christian Hesse, Ole Jensen, Tom Heskes
2009	B	`conf`	Multi-task Preference learning with Gaussian Processes. ESANN Adriana Birlutiu, Perry Groot, Tom Heskes
2009	J	`jnl`	Selecting features for BCI control based on a covert spatial attention paradigm. Neural Networks Marcel van Gerven, Ali Bahramisharif, Tom Heskes, Ole Jensen
2008	A	`conf`	Bounds on the Bethe Free Energy for Gaussian Networks. UAI Botond Cseke, Tom Heskes
2007	—	`conf`	Expectation Propagation for Rating Players in Sports Competitions. PKDD Adriana Birlutiu, Tom Heskes
2007	J	`jnl`	Learning and approximate inference in dynamic hierarchical models. Comput. Stat. Data Anal. Bart Bakker, Tom Heskes
2007	J	`jnl`	Predicting carcinoid heart disease with the noisy-threshold classifier. Artif. Intell. Medicine Marcel van Gerven, Rasa Jurgelenaite, Babs G. Taal, Tom Heskes, Peter J. F. Lucas
2007	—	`conf`	Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach. NIPS José Miguel Hernández-Lobato, Tjeerd Dijkstra, Tom Heskes
2006	J	`jnl`	Convexity Arguments for Efficient Minimization of the Bethe and Kikuchi Free Energies. J. Artif. Intell. Res. Tom Heskes
2006	J	`jnl`	Deterministic approximate inference techniques for conditionally Gaussian state space models. Stat. Comput. Onno Zoeter, Tom Heskes
2006	—	`conf`	EM Algorithm for Symmetric Causal Independence Models. ECML Rasa Jurgelenaite, Tom Heskes
2006	—	`conf`	Symmetric Causal Independence Models for Classification. Probabilistic Graphical Models Rasa Jurgelenaite, Tom Heskes
2005	J	`jnl`	Change Point Problems in Linear Dynamical Systems. J. Mach. Learn. Res. Onno Zoeter, Tom Heskes
2005	—	`conf`	Exploring the Noisy Threshold Function in Designing Bayesian Networks. SGAI Conf. Rasa Jurgelenaite, Peter J. F. Lucas, Tom Heskes
2005	A	`conf`	Gaussian Quadrature Based Expectation Propagation. AISTATS Onno Zoeter, Tom Heskes
2005	—	`conf`	Gaussian Quadrature Based Expectation Propagation. BNAIC Onno Zoeter, Tom Heskes
2005	—	`conf`	Incremental Utility Elicitation for Adaptive Personalization. BNAIC Tom Heskes, Bert de Vries
2005	J	`jnl`	Novel approximations for inference in nonlinear dynamical systems using expectation propagation. Neurocomputing Alexander Ypma, Tom Heskes
2005	—	`conf`	Use of the Noisy Threshold Function in Building Bayesian Networks. BNAIC Rasa Jurgelenaite, Peter J. F. Lucas, Tom Heskes
2004	B	`conf`	Novel approximations for inference and learning in nonlinear dynamical systems. ESANN Alexander Ypma, Tom Heskes
2004	J	`jnl`	On the Uniqueness of Loopy Belief Propagation Fixed Points. Neural Comput. Tom Heskes
2003	—	`conf`	Approximate Expectation Maximization. NIPS Tom Heskes, Onno Zoeter, Wim Wiegerinck
2003	A	`conf`	Approximate Inference and Constrained Optimization. UAI Tom Heskes, Kees Albers, Bert Kappen
2003	J	`jnl`	Clustering ensembles of neural network models. Neural Networks Bart Bakker, Tom Heskes
2003	A	`conf`	Generalized belief propagation for approximate inference in hybrid Bayesian networks. AISTATS Tom Heskes, Onno Zoeter
2003	J	`jnl`	Hierarchical Visualization of Time-Series Data Using Switching Linear Dynamical Systems. IEEE Trans. Pattern Anal. Mach. Intell. Onno Zoeter, Tom Heskes
2003	—	`conf`	Iterated extended Kalman smoothing with expectation-propagation. NNSP Alexander Ypma, Tom Heskes
2003	C	`conf`	Multi-scale Switching Linear Dynamical Systems. ICANN Onno Zoeter, Tom Heskes
2003	J	`jnl`	Optimising newspaper sales using neural-Bayesian technology. Neural Comput. Appl. Tom Heskes, Jan-Joost Spanjers, Bart Bakker, Wim Wiegerinck
2003	J	`jnl`	Task Clustering and Gating for Bayesian Multitask Learning. J. Mach. Learn. Res. Bart Bakker, Tom Heskes
2002	J	`jnl`	Approximate algorithms for neural-Bayesian approaches. Theor. Comput. Sci. Tom Heskes, Bart Bakker, Bert Kappen
2002	—	`conf`	Automatic Categorization of Web Pages and User Clustering with Mixtures of Hidden Markov Models. WebKDD Alexander Ypma, Tom Heskes
2002	A	`conf`	Expectation Propogation for Approximate Inference in Dynamic Bayesian Networks. UAI Tom Heskes, Onno Zoeter
2002	—	`conf`	Fractional Belief Propagation. NIPS Wim Wiegerinck, Tom Heskes
2002	A	`conf`	IPF for Discrete Chain Factor Graphs. UAI Wim Wiegerinck, Tom Heskes
2002	C	`conf`	Model Clustering for Neural Network Ensembles. ICANN Bart Bakker, Tom Heskes
2002	—	`conf`	Stable Fixed Points of Loopy Belief Propagation Are Local Minima of the Bethe Free Energy. NIPS Tom Heskes
2001	J	`jnl`	Self-organizing maps, vector quantization, and mixture modeling. IEEE Trans. Neural Networks Tom Heskes
2000	—	`conf`	EM Algorithms for Self-Organizing Maps. IJCNN (6) Tom Heskes, Jan-Joost Spanjers, Wim Wiegerinck
2000	A*	`conf`	Empirical Bayes for Learning to Learn. ICML Tom Heskes
2000	B	`conf`	General Bias/Variance Decomposition with Target Independent Variance of Error Functions Derived from the Exponential Family of Distributions. ICPR Jakob Vogdrup Hansen, Tom Heskes
2000	J	`jnl`	Input selection based on an ensemble. Neurocomputing Piërre van de Laar, Tom Heskes
2000	J	`jnl`	On "Natural" Learning and Pruning in Multilayered Perceptrons. Neural Comput. Tom Heskes
2000	—	`conf`	Survival Analysis: A Neural-Bayesian Approach. ANNIMAB Bart J. Bakker, Bert Kappen, Tom Heskes
1999	B	`conf`	Model clustering by deterministic annealing. ESANN Bart Bakker, Tom Heskes
1999	J	`jnl`	Partial Retraining: A New Approach to Input Relevance Determination. Int. J. Neural Syst. Piërre van de Laar, Tom Heskes, Stan C. A. M. Gielen
1999	J	`jnl`	Pruning Using Parameter and Neuronal Metrics. Neural Comput. Piërre van de Laar, Tom Heskes
1998	J	`jnl`	Bias/Variance Decompositions for Likelihood-Based Estimators. Neural Comput. Tom Heskes
1998	A*	`conf`	Solving a Huge Number of Similar Tasks: A Combination of Multi-Task Learning and a Hierarchical Bayesian Approach. ICML Tom Heskes
1997	C	`conf`	Input Selection with Partial Retraining. ICANN Piërre van de Laar, Stan C. A. M. Gielen, Tom Heskes
1997	—	`conf`	Selecting Weighting Factors in Logarithmic Opinion Pools. NIPS Tom Heskes
1997	J	`jnl`	Task-Dependent Learning of Attention. Neural Networks Piërre van de Laar, Tom Heskes, Stan C. A. M. Gielen
1996	J	`jnl`	A theoretical comparison of batch-mode, on-line, cyclic, and almost-cyclic learning. IEEE Trans. Neural Networks Tom Heskes, Wim Wiegerinck
1996	—	`conf`	Balancing Between Bagging and Bumping. NIPS Tom Heskes
1996	J	`jnl`	How Dependencies between Successive Examples Affect On-Line Learning. Neural Comput. Wim Wiegerinck, Tom Heskes
1996	—	`conf`	Practical Confidence and Prediction Intervals. NIPS Tom Heskes
1996	J	`jnl`	Transition times in self-organizing maps. Biol. Cybern. Tom Heskes
1995	—	`conf`	A Neural Model of Visual Attention. SNN Symposium on Neural Networks Piërre van de Laar, Tom Heskes, Stan C. A. M. Gielen
1995	—	`conf`	Output Coding and Modularity for Multi-Class Problems. SNN Symposium on Neural Networks André Pastoors, Tom Heskes
1994	B	`conf`	Stochastics of on-line back-propagation. ESANN Tom Heskes
1993	—	`conf`	Error potentials for self-organization. ICNN Tom Heskes, Bert Kappen
1992	J	`jnl`	Retrieval of pattern sequences at variable speeds in a neural network with delays. Neural Networks Tom Heskes, Stan C. A. M. Gielen