267 citations · 359 across the 7 of their papers we have counts for
13 papers
Fixing Model Bugs with Natural Language Patches
Shikhar Murty, Christopher D. Manning, Scott Lundberg +1
Current approaches for fixing systematic problems in NLP models (e.g. regex patches, finetuning on more data) are either brittle, or labor-intensive and liable to shortcuts. In con…
Shapley Flow: A Graph-based Approach to Interpreting Model Predictions
Jiaxuan Wang, Jenna Wiens, Scott Lundberg
Many existing approaches for estimating feature importance are problematic because they ignore or hide dependencies among features. A causal graph, which encodes the relationships…
True to the Model or True to the Data?
Hugh Chen, Joseph D. Janizek, Scott Lundberg +1
A variety of recent papers discuss the application of Shapley values, a concept for explaining coalitional games, for feature attribution in machine learning. However, the correct…
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…
Forecasting adverse surgical events using self-supervised transfer learning for physiological signals
Hugh Chen, Scott Lundberg, Gabe Erion +2
Hundreds of millions of surgical procedures take place annually across the world, which generate a prevalent type of electronic health record (EHR) data comprising time series phys…
Explaining Models by Propagating Shapley Values of Local Components
Hugh Chen, Scott Lundberg, Su-In Lee
In healthcare, making the best possible predictions with complex models (e.g., neural networks, ensembles/stacks of different models) can impact patient welfare. In order to make t…