6 citations · 6 across the 10 of their papers we have counts for
4 papers · 1 filter
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…
Transcoders Find Interpretable LLM Feature Circuits
Jacob Dunefsky, Philippe Chlenski, Neel Nanda
A key goal in mechanistic interpretability is circuit analysis: finding sparse subgraphs of models corresponding to specific behaviors or capabilities. However, MLP sublayers make…
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…