49 citations · 55 across the 2 of their papers we have counts for
4 papers
Improving KernelSHAP: Practical Shapley Value Estimation via Linear Regression
Ian Covert, Su-In Lee
The Shapley value concept from cooperative game theory has become a popular technique for interpreting ML models, but efficiently estimating these values remains challenging, parti…
Understanding Global Feature Contributions With Additive Importance Measures
Ian Covert, Scott Lundberg, Su-In Lee
Understanding the inner workings of complex machine learning models is a long-standing problem and most recent research has focused on local interpretability. To assess the role of…
Temporal Graph Convolutional Networks for Automatic Seizure Detection
Ian Covert, Balu Krishnan, Imad Najm +4
Seizure detection from EEGs is a challenging and time consuming clinical problem that would benefit from the development of automated algorithms. EEGs can be viewed as structural t…
Neural Granger Causality
Alex Tank, Ian Covert, Nicholas Foti +2
While most classical approaches to Granger causality detection assume linear dynamics, many interactions in real-world applications, like neuroscience and genomics, are inherently…