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Found 3 papers, 1 papers with code
Model-agnostic variable importance for predictive uncertainty: an entropy-based approach
In particular, by adapting permutation feature importance, partial dependence plots, and individual conditional expectation plots, we demonstrate that novel insights into model behaviour may be obtained and that these methods can be used to measure the impact of features on both the entropy of the predictive distribution and the log-likelihood of the ground truth labels under that distribution.
SonOpt: Sonifying Bi-objective Population-Based Optimization Algorithms
The benefits of data sonification have been shown for various non-optimization related monitoring tasks.
Fully Bayesian Recurrent Neural Networks for Safe Reinforcement Learning
Reinforcement Learning (RL) has demonstrated state-of-the-art results in a number of autonomous system applications, however many of the underlying algorithms rely on black-box predictions.
