ARTree: A Deep Autoregressive Model for Phylogenetic Inference
Tianyu Xie Cheng Zhang
Designing flexible probabilistic models over tree topologies is important for
developing efficient phylogenetic inference methods. To do that, previous works
often leverage the similarity of tree topologies via hand-engineered heuristic
features which would require pre-sampled tree topologies and may suffer from
limited approximation capability. In this paper, we propose a deep
autoregressive model for phylogenetic inference based on graph neural networks
(GNNs), called ARTree. By decomposing a tree topology into a sequence of leaf
node addition operations and modeling the involved conditional distributions
based on learnable topological features via GNNs, ARTree can provide a rich
family of distributions over the entire tree topology space that have simple
sampling algorithms and density estimation procedures, without using heuristic
features. We demonstrate the effectiveness and efficiency of our method on a
benchmark of challenging real data tree topology density estimation and
variational Bayesian phylogenetic inference problems.