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Found 4 papers, 2 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.
A max-affine spline approximation of neural networks using the Legendre transform of a convex-concave representation
This work presents a novel algorithm for transforming a neural network into a spline representation.
A Unified Theory of Diversity in Ensemble Learning
We present a theory of ensemble diversity, explaining the nature of diversity for a wide range of supervised learning scenarios.
Bias-Variance Decompositions for Margin Losses
We introduce a novel bias-variance decomposition for a range of strictly convex margin losses, including the logistic loss (minimized by the classic LogitBoost algorithm), as well as the squared margin loss and canonical boosting loss.
