270 citations · 669 across the 12 of their papers we have counts for
12 papers
Assessing biomedical knowledge robustness in large language models by query-efficient sampling attacks
R. Patrick Xian, Alex J. Lee, Satvik Lolla +4
The increasing depth of parametric domain knowledge in large language models (LLMs) is fueling their rapid deployment in real-world applications. Understanding model vulnerabilitie…
Contrastive Corpus Attribution for Explaining Representations
Chris Lin, Hugh Chen, Chanwoo Kim +1
Despite the widespread use of unsupervised models, very few methods are designed to explain them. Most explanation methods explain a scalar model output. However, unsupervised mode…
Algorithms to estimate Shapley value feature attributions
Hugh Chen, Ian C. Covert, Scott M. Lundberg +1
Feature attributions based on the Shapley value are popular for explaining machine learning models; however, their estimation is complex from both a theoretical and computational s…
Explaining a Series of Models by Propagating Shapley Values
Hugh Chen, Scott M. Lundberg, Su-In Lee
Local feature attribution methods are increasingly used to explain complex machine learning models. However, current methods are limited because they are extremely expensive to com…
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…
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…