
LargeScale Gene Network Causal Inference with Bayes Factors of Covariance Structures (BFCS)
Gene regulatory networks play a crucial role in controlling an organism'...
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Constraining the Parameters of HighDimensional Models with Active Learning
Constraining the parameters of physical models with >510 parameters is ...
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A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks
Gene regulatory networks play a crucial role in controlling an organism'...
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A Novel Bayesian Approach for Latent Variable Modeling from Mixed Data with Missing Values
We consider the problem of learning parameters of latent variable models...
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Stable specification search in structural equation model with latent variables
In our previous study, we introduced stable specification search for cro...
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Robust Causal Estimation in the LargeSample Limit without Strict Faithfulness
Causal effect estimation from observational data is an important and muc...
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Deep Multiscale Locationaware 3D Convolutional Neural Networks for Automated Detection of Lacunes of Presumed Vascular Origin
Lacunes of presumed vascular origin (lacunes) are associated with an inc...
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Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities
The anatomical location of imaging features is of crucial importance for...
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Causality on Longitudinal Data: Stable Specification Search in Constrained Structural Equation Modeling
A typical problem in causal modeling is the instability of model structu...
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Regularizing Solutions to the MEG Inverse Problem Using SpaceTime Separable Covariance Functions
In magnetoencephalography (MEG) the conventional approach to source reco...
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The Artificial Mind's Eye: Resisting Adversarials for Convolutional Neural Networks using Internal Projection
We introduce a novel artificial neural network architecture that integra...
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Causality on CrossSectional Data: Stable Specification Search in Constrained Structural Equation Modeling
Causal modeling has long been an attractive topic for many researchers a...
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Proof Supplement  Learning Sparse Causal Models is not NPhard (UAI2013)
This article contains detailed proofs and additional examples related to...
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Properties of Bethe Free Energies and Message Passing in Gaussian Models
We address the problem of computing approximate marginals in Gaussian pr...
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Cyclic Causal Discovery from Continuous Equilibrium Data
We propose a method for learning cyclic causal models from a combination...
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Learning Sparse Causal Models is not NPhard
This paper shows that causal model discovery is not an NPhard problem, ...
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Semisupervised Ranking Pursuit
We propose a novel sparse preference learning/ranking algorithm. Our alg...
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Sparse Approximate Inference for SpatioTemporal Point Process Models
Spatiotemporal point process models play a central role in the analysis...
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IPF for Discrete Chain Factor Graphs
Iterative Proportional Fitting (IPF), combined with EM, is commonly used...
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Expectation Propogation for approximate inference in dynamic Bayesian networks
We describe expectation propagation for approximate inference in dynamic...
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Approximate Inference and Constrained Optimization
Loopy and generalized belief propagation are popular algorithms for appr...
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A Bayesian Approach to Constraint Based Causal Inference
We target the problem of accuracy and robustness in causal inference fro...
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A Logical Characterization of ConstraintBased Causal Discovery
We present a novel approach to constraintbased causal discovery, that t...
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Premise Selection for Mathematics by Corpus Analysis and Kernel Methods
Smart premise selection is essential when using automated reasoning as a...
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Tom Heskes
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Professor in Artificial Intelligence at Radboud University Nijmegen, CoFounder at Machine2Learn.