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Publikováno v:
Proceedings of the AAAI Conference on Artificial Intelligence. 35:11433-11441
Instance-based model-agnostic feature importance explanations (LIME, SHAP, L2X) are a popular form of algorithmic transparency. These methods generally return either a weighting or subset of input features as an explanation for the classification of
Publikováno v:
2009 Annual Conference & Exposition Proceedings.
The need to calibrate increasingly complex statistical models requires a persistent effort for further advances on available, computationally intensive Monte Carlo methods. We study here an advanced version of familiar Markov Chain Monte Carlo (MCMC)
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