| Inconsistency of Bayesian inference for misspecified linear models, and a proposal for repairing it P Grünwald, T van Ommen Bayesian Analysis 12 (4), 1069-1103, 2017 | 308 | 2017 |
| Domain Adaptation by Using Causal Inference to Predict Invariant Conditional Distributions S Magliacane, T van Ommen, T Claassen, S Bongers, P Versteeg, ... Advances in Neural Information Processing Systems, 10869-10879, 2018 | 253 | 2018 |
| Algebraic Equivalence of Linear Structural Equation Models T van Ommen, JM Mooij Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence …, 2017 | 15 | 2017 |
| Robust probability updating T van Ommen, WM Koolen, TE Feenstra, PD Grünwald International Journal of Approximate Reasoning 74, 30-57, 2016 | 6 | 2016 |
| Better predictions when models are wrong or underspecified M Ommen Faculty of Science, Leiden University, 2015 | 6 | 2015 |
| Causal Entropy and Information Gain for Measuring Causal Control FNFQ Simoes, M Dastani, T van Ommen European Conference on Artificial Intelligence, 216-231, 2023 | 4 | 2023 |
| Graphical Representations for Algebraic Constraints of Linear Structural Equations Models T van Ommen, M Drton International Conference on Probabilistic Graphical Models, 409-420, 2022 | 2 | 2022 |
| Robust Causal Domain Adaptation in a Simple Diagnostic Setting T van Ommen International Symposium on Imprecise Probabilities: Theories and …, 2019 | 2 | 2019 |
| Computing Minimax Decisions with Incomplete Observations T van Ommen Proceedings of the Tenth International Symposium on Imprecise Probability …, 2017 | 2 | 2017 |
| Fundamental Properties of Causal Entropy and Information Gain FNFQ Simoes, M Dastani, T van Ommen Causal Learning and Reasoning, 188-208, 2024 | 1 | 2024 |
| Risk-based decision making: estimands for sequential prediction under interventions K Luijken, P Morzywołek, W van Amsterdam, G Cinà, J Hoogland, ... arXiv preprint arXiv:2311.17547, 2023 | 1 | 2023 |
| Learning Bayesian Networks by Branching on Constraints T van Ommen International Conference on Probabilistic Graphical Models, 511-522, 2018 | 1 | 2018 |
| Optimal Causal Representations and the Causal Information Bottleneck FNFQ Simoes, M Dastani, T van Ommen arXiv preprint arXiv:2410.00535, 2024 | | 2024 |
| Efficiently Deciding Algebraic Equivalence of Bow-Free Acyclic Path Diagrams T van Ommen arXiv preprint arXiv:2406.09049, 2024 | | 2024 |
| Causal blind spots when using prediction models for treatment decisions N van Geloven, RH Keogh, W van Amsterdam, G Cinà, JH Krijthe, N Peek, ... arXiv preprint arXiv:2402.17366, 2024 | | 2024 |
| Efficient Algorithms for Minimax Decisions Under Tree-Structured Incompleteness T van Ommen, WM Koolen, PD Grünwald Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 15th …, 2019 | | 2019 |
| Combining predictions from linear models when training and test inputs differ T van Ommen Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence …, 2014 | | 2014 |
| AIC for Conditional Model Selection T van Ommen BENELEARN 2013, 2013 | | 2013 |
| ADAPTING AIC TO CONDITIONAL MODEL SELECTION T van Ommen FIFTH WORKSHOP ON INFORMATION THEORETIC METHODS IN SCIENCE AND ENGINEERING, 4, 2012 | | 2012 |
| The Assignment Problem on Sets of Strings T van Ommen LIACS, Universiteit Leiden, 2012 | | 2012 |
Peter GrünwaldGroup Leader Machine Learning, CWI; Professor of Statistics, Leiden UniversityVerified email at cwi.nl
