Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space
Abstract
Hyperbolic space naturally encodes hierarchical structures such as phylogenies (binary trees), where inward-bending geodesics reflect paths through least common ancestors, and the exponential growth of neighborhoods mirrors the super-exponential scaling of topologies. This scaling challenge limits the efficiency of Euclidean-based approximate Bayesian inference methods. Motivated by the geometric connections between trees and hyperbolic space, we develop novel hyperbolic extensions of two sequential search algorithms: Combinatorial and Nested Combinatorial Sequential Monte Carlo (\textsc{Csmc} and \textsc{Ncsmc}). Our approach introduces consistent and unbiased estimators, along with variational inference methods (\textsc{H-Vcsmc} and \textsc{H-Vncsmc}), which outperform their Euclidean counterparts. Empirical results demonstrate improved speed, scalability and performance in high-dimensional Bayesian phylogenetic inference tasks.
Cite
Text
Chen et al. "Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space." Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, 2025.Markdown
[Chen et al. "Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space." Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, 2025.](https://mlanthology.org/aistats/2025/chen2025aistats-variational/)BibTeX
@inproceedings{chen2025aistats-variational,
title = {{Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space}},
author = {Chen, Alex and Chlenski, Philippe and Munyuza, Kenneth and Moretti, Antonio Khalil and Naesseth, Christian A. and Pe’er, Itsik},
booktitle = {Proceedings of The 28th International Conference on Artificial Intelligence and Statistics},
year = {2025},
pages = {2962-2970},
volume = {258},
url = {https://mlanthology.org/aistats/2025/chen2025aistats-variational/}
}