Causal Discovery with Continuous Additive Noise Models
Type-II Errors of Independence Tests Can Lead to Arbitrarily Large Errors in Estimated Causal Effects: An Illustrative Example
Proceedings of the UAI 2014 Workshop Causal Inference: Learning and Prediction (UAI2014CI)
Proceedings of the UAI 2014 Workshop Causal Inference: Learning and Prediction
[1111 KB pdf] [BibTeX]
2013:
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Cyclic Causal Discovery from Continuous Equilibrium DataProceedings of the 29th Annual Conference on Uncertainty in Artificial Intelligence (UAI-13) (UAI 2013)
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From Ordinary Differential Equations to Structural Causal Models: the deterministic caseProceedings of the 29th Annual Conference on Uncertainty in Artificial Intelligence (UAI-13) (UAI 2013)
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Learning Sparse Causal Models is not NP-HardProceedings of the 29th Annual Conference on Uncertainty in Artificial Intelligence (UAI-13) (UAI 2013)
2012:
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On causal and anticausal learningProceedings of the 29th Annual International Conference on Machine Learning (ICML 2012)
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Information-geometric approach to inferring causal directions
2011:
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On Causal Discovery with Cyclic Additive Noise ModelsAdvances in Neural Information Processing Systems 24 (NIPS*2011)
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Efficient inference in matrix-variate Gaussian models with iid observation noiseAdvances in Neural Information Processing Systems 24 (NIPS*2011)
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Learning of causal relationsProceedings of the 19th European Symposium on Artificial Neural Networks (ESANN 2011)
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Identifiability of Causal Graphs using Functional ModelsProceedings of the 27th Annual Conference on Uncertainty in Artificial Intelligence (UAI-11) (UAI 2011)
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A Graphical Model Framework for Decoding in the Visual ERP-Based BCI Speller
2010:
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Probabilistic latent variable models for distinguishing between cause and effectAdvances in Neural Information Processing Systems 23 (NIPS*2010)
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libDAI: A Free and Open Source C++ Library for Discrete Approximate Inference in Graphical ModelsSee also the libDAI webpage.
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Inferring deterministic causal relationsProceedings of the 26th Annual Conference on Uncertainty in Artificial Intelligence (UAI-10) (UAI 2010)(Best student paper award)
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Remote Sensing Feature Selection by Kernel Dependence MeasuresIEEE Geoscience and Remote Sensing Letters 7(3):587-591, July 2010(IEEE Geoscience and Remote Sensing Society 2011 Letters Prize Paper Award)
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Distinguishing between cause and effectJournal of Machine Learning Research Workshop & Conference Proceedings 6(Feb):147-156, 2010
2009:
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Identifying confounders using additive noise modelsProceedings of the 25th Annual Conference on Uncertainty in Artificial Intelligence (UAI-09) (UAI 2009)
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Regression by dependence minimization and its application to causal inferenceProceedings of the 26th Annual International Conference on Machine Learning (ICML 2009) (ICML 2009)
2008:
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Nonlinear causal discovery with additive noise modelsAdvances in Neural Information Processing Systems 21 (NIPS*2008)(Corollary 2 which appeared in a previous version of this paper has been removed because it contained an error)
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Bounds on marginal probability distributionsAdvances in Neural Information Processing Systems 21 (NIPS*2008)
2007:
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Sufficient Conditions for Convergence of the Sum-Product AlgorithmIEEE Transactions on Information Theory 53(12):4422-4437, Dec. 2007
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Truncating the Loop Series Expansion for Belief PropagationJournal of Machine Learning Research 8(Sep):1987-2016, 2007
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Loop Corrections for Approximate Inference on Factor GraphsJournal of Machine Learning Research 8(May):1113-1143, 2007
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Inference in the Promedas medical expert systemProceedings of the 11th Conference on Artificial Intelligence in Medicine (AIME 07)
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Loop Corrected Belief PropagationProceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-07)
2005:
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Sufficient conditions for convergence of Loopy Belief PropagationProceedings of the 21th Annual Conference on Uncertainty in Artificial Intelligence (UAI-05)
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On the properties of the Bethe approximation and Loopy Belief Propagation on binary networks
2004:
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Validity Estimates for Loopy Belief Propagation on Binary Real-world NetworksAdvances in Neural Information Processing Systems 17 (NIPS*2004)
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Quantitative Imaging through a Spectrograph. 1. Principles and TheoryApplied Optics Volume 43, Issue 30, pp. 5669-5681 (October 2004)
Books
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Understanding and Improving Belief PropagationPhD thesis May 7, 2008
Technical reports
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Proof Supplement - Learning Sparse Causal Models is not NP-hard (UAI2013)
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Novel Bounds on Marginal ProbabilitiesarXiv:0801.3797v1, submitted to JMLR
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Loop corrections for approximate inferencearXiv:cs/0612030v1, technical report
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Spin-glass phase transitions on real-world graphs