Heskes, Tom

52 publications

MLJ 2025 Likelihood-Ratio-Based Confidence Intervals for Neural Networks Laurens Sluijterman, Eric Cator, Tom Heskes

UAI 2025 Offline Changepoint Detection with Gaussian Processes Janneke Verbeek, Tom Heskes, Yuliya Shapovalova

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

ICLRW 2024 Heteroscedastic Uncertainty Quantification in Physics-Informed Neural Networks Olivier Claessen, Yuliya Shapovalova, Tom Heskes

JMLR 2024 Unsupervised Anomaly Detection Algorithms on Real-World Data: How Many Do We Need? Roel Bouman, Zaharah Bukhsh, Tom Heskes

CLeaR 2023 Beyond the Markov Equivalence Class: Extending Causal Discovery Under Latent Confounding Mirthe Maria Van Diepen, Ioan Gabriel Bucur, Tom Heskes, Tom Claassen

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

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

PGM 2018 A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks Ioan Gabriel Bucur, Tom Bussel, Tom Claassen, Tom Heskes

MLJ 2018 A Scalable Preference Model for Autonomous Decision-Making Markus Peters, Maytal Saar-Tsechansky, Wolfgang Ketter, Sinead A. Williamson, Perry Groot, Tom Heskes

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

AISTATS 2017 Robust Causal Estimation in the Large-Sample Limit Without Strict Faithfulness Ioan Gabriel Bucur, Tom Claassen, Tom Heskes

PGM 2016 Computing Lower and Upper Bounds on the Probability of Causal Statements Elena Sokolova, Martine Hoogman, Perry Groot, Tom Claassen, Tom Heskes

ECML-PKDD 2016 Copula PC Algorithm for Causal Discovery from Mixed Data Ruifei Cui, Perry Groot, Tom Heskes

UAI 2015 Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, UAI 2015, July 12-16, 2015, Amsterdam, the Netherlands Marina Meila, Tom Heskes

CVPRW 2013 Automatic Signer Diarization - The Mover Is the Signer Approach Binyam Gebrekidan Gebre, Peter Wittenburg, Tom Heskes

IJCAI 2013 Bayesian Probabilities for Constraint-Based Causal Discovery Tom Claassen, Tom Heskes

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

MLJ 2013 Efficiently Learning the Preferences of People Adriana Birlutiu, Perry Groot, Tom Heskes

UAI 2013 Learning Sparse Causal Models Is Not NP-Hard Tom Claassen, Joris M. Mooij, Tom Heskes

UAI 2012 A Bayesian Approach to Constraint Based Causal Inference Tom Claassen, Tom Heskes

UAI 2011 A Logical Characterization of Constraint-Based Causal Discovery Tom Claassen, Tom Heskes

JMLR 2011 Approximate Marginals in Latent Gaussian Models Botond Cseke, Tom Heskes

NeurIPS 2011 On Causal Discovery with Cyclic Additive Noise Models Joris M. Mooij, Dominik Janzing, Tom Heskes, Bernhard Schölkopf

JAIR 2011 Properties of Bethe Free Energies and Message Passing in Gaussian Models Botond Cseke, Tom Heskes

NeurIPS 2010 Causal Discovery in Multiple Models from Different Experiments Tom Claassen, Tom Heskes

AISTATS 2010 Improving Posterior Marginal Approximations in Latent Gaussian Models Botond Cseke, Tom Heskes

NeurIPS 2009 Bayesian Source Localization with the Multivariate Laplace Prior Marcel V. Gerven, Botond Cseke, Robert Oostenveld, Tom Heskes

UAI 2008 Bounds on the Bethe Free Energy for Gaussian Networks Botond Cseke, Tom Heskes

NeurIPS 2007 Regulator Discovery from Gene Expression Time Series of Malaria Parasites: A Hierachical Approach José M. Hernández-lobato, Tjeerd Dijkstra, Tom Heskes

JAIR 2006 Convexity Arguments for Efficient Minimization of the Bethe and Kikuchi Free Energies Tom Heskes

ECML-PKDD 2006 EM Algorithm for Symmetric Causal Independence Models Rasa Jurgelenaite, Tom Heskes

JMLR 2005 Change Point Problems in Linear Dynamical Systems Onno Zoeter, Tom Heskes

AISTATS 2005 Gaussian Quadrature Based Expectation Propagation Onno Zoeter, Tom Heskes

NeCo 2004 On the Uniqueness of Loopy Belief Propagation Fixed Points Tom Heskes

NeurIPS 2003 Approximate Expectation Maximization Tom Heskes, Onno Zoeter, Wim Wiegerinck

UAI 2003 Approximate Inference and Constrained Optimization Tom Heskes, Kees Albers, Bert Kappen

AISTATS 2003 Generalized Belief Propagation for Approximate Inference in Hybrid Bayesian Networks Tom Heskes, Onno Zoeter

JMLR 2003 Task Clustering and Gating for Bayesian Multitask Learning Bart Bakker, Tom Heskes

UAI 2002 Expectation Propogation for Approximate Inference in Dynamic Bayesian Networks Tom Heskes, Onno Zoeter

NeurIPS 2002 Fractional Belief Propagation Wim Wiegerinck, Tom Heskes

UAI 2002 IPF for Discrete Chain Factor Graphs Wim Wiegerinck, Tom Heskes

NeurIPS 2002 Stable Fixed Points of Loopy Belief Propagation Are Local Minima of the Bethe Free Energy Tom Heskes

ICML 2000 Empirical Bayes for Learning to Learn Tom Heskes

NeCo 2000 On "Natural" Learning and Pruning in Multilayered Perceptrons Tom Heskes

NeCo 1999 Pruning Using Parameter and Neuronal Metrics Piërre van de Laar, Tom Heskes

NeCo 1998 Bias/Variance Decompositions for Likelihood-Based Estimators Tom Heskes

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

NeurIPS 1997 Selecting Weighting Factors in Logarithmic Opinion Pools Tom Heskes

NeurIPS 1996 Balancing Between Bagging and Bumping Tom Heskes

NeCo 1996 How Dependencies Between Successive Examples Affect On-Line Learning Wim Wiegerinck, Tom Heskes

NeurIPS 1996 Practical Confidence and Prediction Intervals Tom Heskes