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Found 5 papers, 2 papers with code
End-to-end Feature Selection Approach for Learning Skinny Trees
Skinny Trees lead to superior feature selection than many existing toolkits e. g., in terms of AUC performance for $25\%$ feature budget, Skinny Trees outperforms LightGBM by $10. 2\%$ (up to $37. 7\%$), and Random Forests by $3\%$ (up to $12. 5\%$).
COMET: Learning Cardinality Constrained Mixture of Experts with Trees and Local Search
To deal with this challenge, we propose a novel, permutation-based local search method that can complement first-order methods in training any sparse gate, e. g., Hash routing, Top-k, DSelect-k, and COMET.
Flexible Modeling and Multitask Learning using Differentiable Tree Ensembles
We therefore propose a novel tensor-based formulation of differentiable trees that allows for efficient vectorization on GPUs.
Newer is not always better: Rethinking transferability metrics, their peculiarities, stability and performance
In this paper, we show that the statistical problems with covariance estimation drive the poor performance of H-score -- a common baseline for newer metrics -- and propose shrinkage-based estimator.
Predicting Census Survey Response Rates With Parsimonious Additive Models and Structured Interactions
We discuss and interpret findings from our model on the US Census Planning Database.
