267 citations · 381 across the 10 of their papers we have counts for
17 papers
Moment Matching Deep Contrastive Latent Variable Models
Ethan Weinberger, Nicasia Beebe-Wang, Su-In Lee
In the contrastive analysis (CA) setting, machine learning practitioners are specifically interested in discovering patterns that are enriched in a target dataset as compared to a…
Pitfalls of Explainable ML: An Industry Perspective
Sahil Verma, Aditya Lahiri, John P. Dickerson +1
As machine learning (ML) systems take a more prominent and central role in contributing to life-impacting decisions, ensuring their trustworthiness and accountability is of utmost…
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…
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…