Search Results for author:
Found 35 papers, 12 papers with code
Automatic inference of fault tree models via multi-objective evolutionary algorithms
Fault tree analysis is a well-known technique in reliability engineering and risk assessment, which supports decision-making processes and the management of complex systems.
Confident Neural Network Regression with Bootstrapped Deep Ensembles
With the rise of the popularity and usage of neural networks, trustworthy uncertainty estimation is becoming increasingly essential.
Going Grayscale: The Road to Understanding and Improving Unlearnable Examples
Recent work has shown that imperceptible perturbations can be applied to craft unlearnable examples (ULEs), i. e. images whose content cannot be used to improve a classifier during training.
Swift sky localization of gravitational waves using deep learning seeded importance sampling
Fast, highly accurate, and reliable inference of the sky origin of gravitational waves would enable real-time multi-messenger astronomy.
How to Evaluate Uncertainty Estimates in Machine Learning for Regression?
We demonstrate through practical examples that these effects can result in favoring a method, based on the predictive uncertainty, that has undesirable behaviour of the confidence intervals.
Learning Equational Theorem Proving
On these tasks, 3SIL is shown to significantly outperform several established RL and imitation learning methods.
Spectral Ranking of Causal Influence in Complex Systems
Like natural complex systems such as the Earth's climate or a living cell, semiconductor lithography systems are characterized by nonlinear dynamics across more than a dozen orders of magnitude in space and time.
Time Series
Information Theory
Information Theory
Adaptation and Self-Organizing Systems
Physics and Society
MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models
Unfortunately, searching for proper instruments in a many-dimensional set of candidates is a daunting task due to the intractable model space and the fact that we cannot directly test which of these candidates are valid, so most existing search methods either rely on overly stringent modeling assumptions or fail to capture the inherent model uncertainty in the selection process.
Inferring the Direction of a Causal Link and Estimating Its Effect via a Bayesian Mendelian Randomization Approach
In this paper, we propose a Bayesian approach called BayesMR that is a generalization of the Mendelian randomization technique in which we allow for pleiotropic effects and, crucially, for the possibility of reverse causation.
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models
Shapley values underlie one of the most popular model-agnostic methods within explainable artificial intelligence.
Large-Scale Local Causal Inference of Gene Regulatory Relationships
Many of these computational methods are designed to infer individual regulatory relationships among genes from data on gene expression.
Constraining the Parameters of High-Dimensional Models with Active Learning
Constraining the parameters of physical models with $>5-10$ parameters is a widespread problem in fields like particle physics and astronomy.
A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks
Gene regulatory networks play a crucial role in controlling an organism's biological processes, which is why there is significant interest in developing computational methods that are able to extract their structure from high-throughput genetic data.
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 from mixed (continuous and ordinal) data with missing values.
Stable specification search in structural equation model with latent variables
In our previous study, we introduced stable specification search for cross-sectional data (S3C).
Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness
Causal effect estimation from observational data is an important and much studied research topic.
Deep Multi-scale Location-aware 3D Convolutional Neural Networks for Automated Detection of Lacunes of Presumed Vascular Origin
In this paper, we propose an automated two-stage method using deep convolutional neural networks (CNN).
Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities
The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks.
Causality on Longitudinal Data: Stable Specification Search in Constrained Structural Equation Modeling
The present work introduces a novel causal modeling algorithm for longitudinal data, that is robust for finite samples based on recent advances in stability selection using subsampling and selection algorithms.
Regularizing Solutions to the MEG Inverse Problem Using Space-Time Separable Covariance Functions
We apply the method to both simulated and empirical data, and demonstrate the efficiency and generality of our Bayesian source reconstruction approach which subsumes various classical approaches in the literature.
The Artificial Mind's Eye: Resisting Adversarials for Convolutional Neural Networks using Internal Projection
We introduce a novel artificial neural network architecture that integrates robustness to adversarial input in the network structure.
Bigger Buffer k-d Trees on Multi-Many-Core Systems
A buffer k-d tree is a k-d tree variant for massively-parallel nearest neighbor search.
Causality on Cross-Sectional Data: Stable Specification Search in Constrained Structural Equation Modeling
Structure search is performed over Structural Equation Models.
Proof Supplement - Learning Sparse Causal Models is not NP-hard (UAI2013)
The algorithm is an adaptation of the well-known FCI algorithm by (Spirtes et al., 2000) that is also sound and complete, but has worst case complexity exponential in $N$.
Properties of Bethe Free Energies and Message Passing in Gaussian Models
We define the Gaussian fractional Bethe free energy in terms of the moment parameters of the approximate marginals, derive a lower and an upper bound on the fractional Bethe free energy and establish a necessary condition for the lower bound to be bounded from below.
Cyclic Causal Discovery from Continuous Equilibrium Data
We propose a method for learning cyclic causal models from a combination of observational and interventional equilibrium data.
Learning Sparse Causal Models is not NP-hard
This paper shows that causal model discovery is not an NP-hard problem, in the sense that for sparse graphs bounded by node degree k the sound and complete causal model can be obtained in worst case order N^{2(k+2)} independence tests, even when latent variables and selection bias may be present.
Sparse Approximate Inference for Spatio-Temporal Point Process Models
Spatio-temporal point process models play a central role in the analysis of spatially distributed systems in several disciplines.
On Causal Discovery with Cyclic Additive Noise Models
We study a particular class of cyclic causal models, where each variable is a (possibly nonlinear) function of its parents and additive noise.
Causal discovery in multiple models from different experiments
We present the MCI-algorithm as the first method that can infer provably valid causal relations in the large sample limit from different experiments.
Bayesian Source Localization with the Multivariate Laplace Prior
We introduce a novel multivariate Laplace (MVL) distribution as a sparsity promoting prior for Bayesian source localization that allows the specification of constraints between and within sources.
Regulator Discovery from Gene Expression Time Series of Malaria Parasites: a Hierachical Approach
We introduce a hierarchical Bayesian model for the discovery of putative regulators from gene expression data only.
