110 citations · 265 across the 9 of their papers we have counts for
9 papers
Theoretical Behavior of XAI Methods in the Presence of Suppressor Variables
Rick Wilming, Leo Kieslich, Benedict Clark +1
In recent years, the community of 'explainable artificial intelligence' (XAI) has created a vast body of methods to bridge a perceived gap between model 'complexity' and 'interpret…
XAI-TRIS: Non-linear image benchmarks to quantify false positive post-hoc attribution of feature importance
Benedict Clark, Rick Wilming, Stefan Haufe
The field of 'explainable' artificial intelligence (XAI) has produced highly cited methods that seek to make the decisions of complex machine learning (ML) methods 'understandable'…
Evaluating saliency methods on artificial data with different background types
Céline Budding, Fabian Eitel, Kerstin Ritter +1
Over the last years, many 'explainable artificial intelligence' (xAI) approaches have been developed, but these have not always been objectively evaluated. To evaluate the quality…
Efficient Hierarchical Bayesian Inference for Spatio-temporal Regression Models in Neuroimaging
Ali Hashemi, Yijing Gao, Chang Cai +4
Several problems in neuroimaging and beyond require inference on the parameters of multi-task sparse hierarchical regression models. Examples include M/EEG inverse problems, neural…
Scrutinizing XAI using linear ground-truth data with suppressor variables
Rick Wilming, Céline Budding, Klaus-Robert Müller +1
Machine learning (ML) is increasingly often used to inform high-stakes decisions. As complex ML models (e.g., deep neural networks) are often considered black boxes, a wealth of pr…
Correlated Components Analysis - Extracting Reliable Dimensions in Multivariate Data
Lucas C. Parra, Stefan Haufe, Jacek P. Dmochowski
How does one find dimensions in multivariate data that are reliably expressed across repetitions? For example, in a brain imaging study one may want to identify combinations of neu…
Powerful statistical inference for nested data using sufficient summary statistics
Irene Dowding, Stefan Haufe
Hierarchically-organized data arise naturally in many psychology and neuroscience studies. As the standard assumption of independent and identically distributed samples does not ho…
Validity of time reversal for testing Granger causality
Irene Winkler, Danny Panknin, Daniel Bartz +2
Inferring causal interactions from observed data is a challenging problem, especially in the presence of measurement noise. To alleviate the problem of spurious causality, Haufe et…
Modeling sparse connectivity between underlying brain sources for EEG/MEG
Stefan Haufe, Ryota Tomioka, Guido Nolte +2
We propose a novel technique to assess functional brain connectivity in EEG/MEG signals. Our method, called Sparsely-Connected Sources Analysis (SCSA), can overcome the problem of…