47 citations · 127 across the 21 of their papers we have counts for
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Mixed-curvature decision trees and random forests
Philippe Chlenski, Quentin Chu, Raiyan R. Khan +3
Decision trees (DTs) and their random forest (RF) extensions are workhorses of classification and regression in Euclidean spaces. However, algorithms for learning in non-Euclidean…
Mixed-Curvature Decision Trees and Random Forests
Philippe Chlenski, Quentin Chu, Itsik Pe'er
We extend decision tree and random forest algorithms to product space manifolds: Cartesian products of Euclidean, hyperspherical, and hyperbolic manifolds. Such spaces have extreme…
Variational Pseudo Marginal Methods for Jet Reconstruction in Particle Physics
Hanming Yang, Antonio Khalil Moretti, Sebastian Macaluso +3
Reconstructing jets, which provide vital insights into the properties and histories of subatomic particles produced in high-energy collisions, is a main problem in data analyses in…
Distributional bias compromises leave-one-out cross-validation
George I. Austin, Itsik Pe'er, Tal Korem
Cross-validation is a common method for estimating the predictive performance of machine learning models. In a data-scarce regime, where one typically wishes to maximize the number…