5 citations · 7 across the 9 of their papers we have counts for
3 papers · 1 filter
Higher-Order Feature Attribution: Bridging Statistics, Explainable AI, and Topological Signal Processing
Kurt Butler, Guanchao Feng, Petar Djuric
Feature attributions are post-training analysis methods that assess how various input features of a machine learning model contribute to an output prediction. Their interpretation…
A Shift in Perspective on Causality in Domain Generalization
Damian Machlanski, Stephanie Riley, Edward Moroshko +7
The promise that causal modelling can lead to robust AI generalization has been challenged in recent work on domain generalization (DG) benchmarks. We revisit the claims of the cau…
Trustworthy Prediction with Gaussian Process Knowledge Scores
Kurt Butler, Guanchao Feng, Tong Chen +1
Probabilistic models are often used to make predictions in regions of the data space where no observations are available, but it is not always clear whether such predictions are we…