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08/04/2023
Likelihood-ratio-based confidence intervals for neural networks
This paper introduces a first implementation of a novel likelihood-ratio...
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05/01/2023
Unsupervised anomaly detection algorithms on real-world data: how many do we need?
In this study we evaluate 32 unsupervised anomaly detection algorithms o...
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02/17/2023
Optimal Training of Mean Variance Estimation Neural Networks
This paper focusses on the optimal implementation of a Mean Variance Est...
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10/07/2022
Machine Learning Meets The Herbrand Universe
The appearance of strong CDCL-based propositional (SAT) solvers has grea...
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04/06/2022
Automatic inference of fault tree models via multi-objective evolutionary algorithms
Fault tree analysis is a well-known technique in reliability engineering...
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02/22/2022
Confident Neural Network Regression with Bootstrapped Deep Ensembles
With the rise of the popularity and usage of neural networks, trustworth...
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11/25/2021
Going Grayscale: The Road to Understanding and Improving Unlearnable Examples
Recent work has shown that imperceptible perturbations can be applied to...
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11/01/2021
Swift sky localization of gravitational waves using deep learning seeded importance sampling
Fast, highly accurate, and reliable inference of the sky origin of gravi...
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06/07/2021
How to Evaluate Uncertainty Estimates in Machine Learning for Regression?
As neural networks become more popular, the need for accompanying uncert...
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02/10/2021
Learning Equational Theorem Proving
We develop Stratified Shortest Solution Imitation Learning (3SIL) to lea...
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12/24/2020
Spectral Ranking of Causal Influence in Complex Systems
Like natural complex systems such as the Earth's climate or a living cel...
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12/18/2020
Inferring the Direction of a Causal Link and Estimating Its Effect via a Bayesian Mendelian Randomization Approach
The use of genetic variants as instrumental variables - an approach know...
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12/18/2020
MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models
The recent availability of huge, many-dimensional data sets, like those ...
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11/03/2020
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models
Shapley values underlie one of the most popular model-agnostic methods w...
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09/03/2019
Large-Scale 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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05/19/2019
Constraining the Parameters of High-Dimensional Models with Active Learning
Constraining the parameters of physical models with >5-10 parameters is ...
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09/18/2018
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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06/12/2018
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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05/24/2018
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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04/06/2017
Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness
Causal effect estimation from observational data is an important and muc...
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10/24/2016
Deep Multi-scale Location-aware 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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10/16/2016
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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05/22/2016
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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04/17/2016
Regularizing Solutions to the MEG Inverse Problem Using Space-Time Separable Covariance Functions
In magnetoencephalography (MEG) the conventional approach to source reco...
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04/15/2016
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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06/18/2015
Causality on Cross-Sectional 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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11/06/2014
Proof Supplement - Learning Sparse Causal Models is not NP-hard (UAI2013)
This article contains detailed proofs and additional examples related to...
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01/16/2014
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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09/26/2013
Cyclic Causal Discovery from Continuous Equilibrium Data
We propose a method for learning cyclic causal models from a combination...
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09/26/2013
Learning Sparse Causal Models is not NP-hard
This paper shows that causal model discovery is not an NP-hard problem, ...
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07/02/2013
Semi-supervised Ranking Pursuit
We propose a novel sparse preference learning/ranking algorithm. Our alg...
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05/17/2013
Sparse Approximate Inference for Spatio-Temporal Point Process Models
Spatio-temporal point process models play a central role in the analysis...
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12/12/2012
IPF for Discrete Chain Factor Graphs
Iterative Proportional Fitting (IPF), combined with EM, is commonly used...
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12/12/2012
Expectation Propogation for approximate inference in dynamic Bayesian networks
We describe expectation propagation for approximate inference in dynamic...
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10/19/2012
Approximate Inference and Constrained Optimization
Loopy and generalized belief propagation are popular algorithms for appr...
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10/16/2012
A Bayesian Approach to Constraint Based Causal Inference
We target the problem of accuracy and robustness in causal inference fro...
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02/14/2012
A Logical Characterization of Constraint-Based Causal Discovery
We present a novel approach to constraint-based causal discovery, that t...
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08/17/2011