Feature attribution for kernel methods is often heuristic and not individualised for each
prediction. To address this, we turn to the concept of Shapley values (SV), a coalition game …

We present a novel approach for explaining Gaussian processes (GPs) that can utilize the
full analytical covariance structure present in GPs. Our method is based on the popular …

We study feature interactions in the context of feature attribution methods for post-hoc
interpretability. In interpretability research, getting to grips with feature interactions is …

Deep learning is quickly becoming a standard approach to solving a range of materials
science objectives, particularly in the field of computer vision. However, labeled datasets …

Shapley values are today extensively used as a model-agnostic explanation framework to
explain complex predictive machine learning models. Shapley values have desirable …

Additive feature explanations using Shapley values have become popular for providing
transparency into the relative importance of each feature to an individual prediction of a …

The replicability crisis has drawn attention to numerous weaknesses in psychology and
social science research practice. In this work we focus on three issues that cannot be …

Fatigue behavior of welded joints is significantly influenced by numerous factors, for
example, local weld geometry. A representative quantity for the influence of the notch effect …

The search for molecular species that are differentially expressed between biological states
is an important step towards discovering promising biomarker candidates. In imaging mass …

Scientists frequently prioritize learning from data rather than training the best possible
model; however, research in machine learning often prioritizes the latter. Marginal …