8 papers
Hyperbolic Genome Embeddings
Raiyan R. Khan, Philippe Chlenski, Itsik Pe'er
Current approaches to genomic sequence modeling often struggle to align the inductive biases of machine learning models with the evolutionarily-informed structure of biological sys…
Manify: A Python Library for Learning Non-Euclidean Representations
Philippe Chlenski, Kaizhu Du, Dylan Satow +2
We present Manify, an open-source Python library for non-Euclidean representation learning. Leveraging manifold learning techniques, Manify provides tools for learning embeddings i…
Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space
Alex Chen, Philipe Chlenski, Kenneth Munyuza +3
Hyperbolic space naturally encodes hierarchical structures such as phylogenies (binary trees), where inward-bending geodesics reflect paths through least common ancestors, and the…
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…
Even Faster Hyperbolic Random Forests: A Beltrami-Klein Wrapper Approach
Philippe Chlenski, Itsik Pe'er
Decision trees and models that use them as primitives are workhorses of machine learning in Euclidean spaces. Recent work has further extended these models to the Lorentz model of…
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…