Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space

Alex Chen, Philippe Chlenski, Kenneth Munyuza, Antonio Khalil Moretti, Christian A. Naesseth, Itsik Pe’er
Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, PMLR 258:2962-2970, 2025.

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 this Paper

BibTeX
@InProceedings{pmlr-v258-chen25f, 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}, pages = {2962--2970}, year = {2025}, editor = {Li, Yingzhen and Mandt, Stephan and Agrawal, Shipra and Khan, Emtiyaz}, volume = {258}, series = {Proceedings of Machine Learning Research}, month = {03--05 May}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v258/main/assets/chen25f/chen25f.pdf}, url = {https://proceedings.mlr.press/v258/chen25f.html}, 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.} }
Endnote
%0 Conference Paper %T Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space %A Alex Chen %A Philippe Chlenski %A Kenneth Munyuza %A Antonio Khalil Moretti %A Christian A. Naesseth %A Itsik Pe’er %B Proceedings of The 28th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2025 %E Yingzhen Li %E Stephan Mandt %E Shipra Agrawal %E Emtiyaz Khan %F pmlr-v258-chen25f %I PMLR %P 2962--2970 %U https://proceedings.mlr.press/v258/chen25f.html %V 258 %X 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.
APA
Chen, A., Chlenski, P., Munyuza, K., Moretti, A.K., Naesseth, C.A. & Pe’er, I.. (2025). Variational Combinatorial Sequential Monte Carlo for Bayesian Phylogenetics in Hyperbolic Space. Proceedings of The 28th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 258:2962-2970 Available from https://proceedings.mlr.press/v258/chen25f.html.

Related Material