31 citations · 64 across the 7 of their papers we have counts for
8 papers
AOI-SSL: Self-Supervised Framework for Efficient Segmentation of Wire-bonded Semiconductors In Optical Inspection
Joaquín Figueira, Rob Van Gastel, Giacomo D'Amicantonio +4
Segmentation models in automated optical inspection of wire-bonded semiconductors are typically device-specific and must be re-trained when new devices or distribution shifts appea…
Inferring the Direction of a Causal Link and Estimating Its Effect via a Bayesian Mendelian Randomization Approach
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
The use of genetic variants as instrumental variables - an approach known as Mendelian randomization - is a popular epidemiological method for estimating the causal effect of an ex…
MASSIVE: Tractable and Robust Bayesian Learning of Many-Dimensional Instrumental Variable Models
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
The recent availability of huge, many-dimensional data sets, like those arising from genome-wide association studies (GWAS), provides many opportunities for strengthening causal in…
Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models
Tom Heskes, Evi Sijben, Ioan Gabriel Bucur +1
Shapley values underlie one of the most popular model-agnostic methods within explainable artificial intelligence. These values are designed to attribute the difference between a m…
Large-Scale Local Causal Inference of Gene Regulatory Relationships
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
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…
A Bayesian Approach for Inferring Local Causal Structure in Gene Regulatory Networks
Ioan Gabriel Bucur, Tom van Bussel, Tom Claassen +1
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…
A Novel Bayesian Approach for Latent Variable Modeling from Mixed Data with Missing Values
Ruifei Cui, Ioan Gabriel Bucur, Perry Groot +1
We consider the problem of learning parameters of latent variable models from mixed (continuous and ordinal) data with missing values. We propose a novel Bayesian Gaussian copula f…
Robust Causal Estimation in the Large-Sample Limit without Strict Faithfulness
Ioan Gabriel Bucur, Tom Claassen, Tom Heskes
Causal effect estimation from observational data is an important and much studied research topic. The instrumental variable (IV) and local causal discovery (LCD) patterns are canon…