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Found 7 papers, 1 papers with code
The Blame Problem in Evaluating Local Explanations, and How to Tackle it
Using this proposed taxonomy, we highlight that all categories of evaluation methods, except those based on the ground truth from interpretable models, suffer from a problem we call the "blame problem."
Can local explanation techniques explain linear additive models?
Local model-agnostic additive explanation techniques decompose the predicted output of a black-box model into additive feature importance scores.
Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths
Instead of using black-box models, such as neural networks, we propose to focus on tree-based LTR models, from which we can extract the ground truth feature importance scores using decision paths.
Evaluating Local Explanations using White-box Models
Evaluating explanation techniques using human subjects is costly, time-consuming and can lead to subjectivity in the assessments.
A study of data and label shift in the LIME framework
LIME is a popular approach for explaining a black-box prediction through an interpretable model that is trained on instances in the vicinity of the predicted instance.
An LP-based hyperparameter optimization model for language modeling
We apply our model to find hyperparameters of a language model and compare it to the grid search algorithm.
Distributed Real-Time Sentiment Analysis for Big Data Social Streams
The real challenge with real-time stream data processing is that it is impossible to store instances of data, and therefore online analytical algorithms are utilized.
