Update publications.bib

assets/misc/publications.bib

@@ -1,3 +1,31 @@
+
+@ARTICLE{Kong2023-jy,
+  title         = "Unsupervised Learning of Phylogenetic Trees via
+                   {Split-Weight} Embedding",
+  author        = "Kong, Yibo and Tiley, George P and Solis-Lemus, Claudia",
+  abstract      = "Unsupervised learning has become a staple in classical
+                   machine learning, successfully identifying clustering
+                   patterns in data across a broad range of domain
+                   applications. Surprisingly, despite its accuracy and elegant
+                   simplicity, unsupervised learning has not been sufficiently
+                   exploited in the realm of phylogenetic tree inference. The
+                   main reason for the delay in adoption of unsupervised
+                   learning in phylogenetics is the lack of a meaningful, yet
+                   simple, way of embedding phylogenetic trees into a vector
+                   space. Here, we propose the simple yet powerful split-weight
+                   embedding which allows us to fit standard clustering
+                   algorithms to the space of phylogenetic trees. We show that
+                   our split-weight embedded clustering is able to recover
+                   meaningful evolutionary relationships in simulated and real
+                   (Adansonia baobabs) data.",
+  month         =  dec,
+  year          =  2023,
+  archivePrefix = "arXiv",
+  primaryClass  = "q-bio.PE",
+  eprint        = "2312.16074"
+}
+
+
 @ARTICLE{Tiley2023-wn,
   title    = "Extracting diamonds: Identifiability of 4-node cycles in level-1
               phylogenetic networks under a pseudolikelihood coalescent model",