Variational Phylogenetic Inference with Products over Bipartitions

Evan Sidrow, Alexandre Bouchard-Cote, Lloyd T Elliott
Proceedings of the 42nd International Conference on Machine Learning, PMLR 267:55533-55555, 2025.

Abstract

Bayesian phylogenetics is vital for understanding evolutionary dynamics, and requires accurate and efficient approximation of posterior distributions over trees. In this work, we develop a variational Bayesian approach for ultrametric phylogenetic trees. We present a novel variational family based on coalescent times of a single-linkage clustering and derive a closed-form density for the resulting distribution over trees. Unlike existing methods for ultrametric trees, our method performs inference over all of tree space, it does not require any Markov chain Monte Carlo subroutines, and our variational family is differentiable. Through experiments on benchmark genomic datasets and an application to the viral RNA of SARS-CoV-2, we demonstrate that our method achieves competitive accuracy while requiring significantly fewer gradient evaluations than existing state-of-the-art techniques.

Cite this Paper

BibTeX
@InProceedings{pmlr-v267-sidrow25a, title = {Variational Phylogenetic Inference with Products over Bipartitions}, author = {Sidrow, Evan and Bouchard-Cote, Alexandre and Elliott, Lloyd T}, booktitle = {Proceedings of the 42nd International Conference on Machine Learning}, pages = {55533--55555}, year = {2025}, editor = {Singh, Aarti and Fazel, Maryam and Hsu, Daniel and Lacoste-Julien, Simon and Berkenkamp, Felix and Maharaj, Tegan and Wagstaff, Kiri and Zhu, Jerry}, volume = {267}, series = {Proceedings of Machine Learning Research}, month = {13--19 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v267/main/assets/sidrow25a/sidrow25a.pdf}, url = {https://proceedings.mlr.press/v267/sidrow25a.html}, abstract = {Bayesian phylogenetics is vital for understanding evolutionary dynamics, and requires accurate and efficient approximation of posterior distributions over trees. In this work, we develop a variational Bayesian approach for ultrametric phylogenetic trees. We present a novel variational family based on coalescent times of a single-linkage clustering and derive a closed-form density for the resulting distribution over trees. Unlike existing methods for ultrametric trees, our method performs inference over all of tree space, it does not require any Markov chain Monte Carlo subroutines, and our variational family is differentiable. Through experiments on benchmark genomic datasets and an application to the viral RNA of SARS-CoV-2, we demonstrate that our method achieves competitive accuracy while requiring significantly fewer gradient evaluations than existing state-of-the-art techniques.} }
Endnote
%0 Conference Paper %T Variational Phylogenetic Inference with Products over Bipartitions %A Evan Sidrow %A Alexandre Bouchard-Cote %A Lloyd T Elliott %B Proceedings of the 42nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2025 %E Aarti Singh %E Maryam Fazel %E Daniel Hsu %E Simon Lacoste-Julien %E Felix Berkenkamp %E Tegan Maharaj %E Kiri Wagstaff %E Jerry Zhu %F pmlr-v267-sidrow25a %I PMLR %P 55533--55555 %U https://proceedings.mlr.press/v267/sidrow25a.html %V 267 %X Bayesian phylogenetics is vital for understanding evolutionary dynamics, and requires accurate and efficient approximation of posterior distributions over trees. In this work, we develop a variational Bayesian approach for ultrametric phylogenetic trees. We present a novel variational family based on coalescent times of a single-linkage clustering and derive a closed-form density for the resulting distribution over trees. Unlike existing methods for ultrametric trees, our method performs inference over all of tree space, it does not require any Markov chain Monte Carlo subroutines, and our variational family is differentiable. Through experiments on benchmark genomic datasets and an application to the viral RNA of SARS-CoV-2, we demonstrate that our method achieves competitive accuracy while requiring significantly fewer gradient evaluations than existing state-of-the-art techniques.
APA
Sidrow, E., Bouchard-Cote, A. & Elliott, L.T.. (2025). Variational Phylogenetic Inference with Products over Bipartitions. Proceedings of the 42nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 267:55533-55555 Available from https://proceedings.mlr.press/v267/sidrow25a.html.

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