URerF uses unsupervised decision forests on sparse linear feature combinations to estimate geodesic distances robustly under high-dimensional noise, outperforming Isomap, UMAP, and FLANN on simulated and connectome data.
Greedy function approximation: a gradient boosting machine,
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2019 2verdicts
UNVERDICTED 2representative citing papers
Treant trains accurate decision tree ensembles that are nearly insensitive to evasion attacks by minimizing an evasion-aware loss via robust splitting and attack invariance.
citing papers explorer
-
Geodesic Learning via Unsupervised Decision Forests
URerF uses unsupervised decision forests on sparse linear feature combinations to estimate geodesic distances robustly under high-dimensional noise, outperforming Isomap, UMAP, and FLANN on simulated and connectome data.
-
Treant: Training Evasion-Aware Decision Trees
Treant trains accurate decision tree ensembles that are nearly insensitive to evasion attacks by minimizing an evasion-aware loss via robust splitting and attack invariance.