Bayesian cluster analysis offers substantial benefits over algorithmic approaches by
providing not only point estimates but also uncertainty in the clustering structure and …

Scientists and engineers are often interested in learning the number of subpopulations (or
components) present in a data set. A common suggestion is to use a finite mixture model …

The rise of machine learning-driven decision-making has sparked a growing emphasis on
algorithmic fairness. Within the realm of clustering, the notion of balance is utilized as a …

We introduce a new multimodal optimization approach called Natural Variational Annealing
(NVA) that combines the strengths of three foundational concepts to simultaneously search …

Bayesian clustering typically relies on mixture models, with each component interpreted as a
different cluster. After defining a prior for the component parameters and weights, Markov …

Many problems in life sciences can be brought back to a comparison of graphs. Even though
a multitude of such techniques exist, often, these assume prior knowledge about the …

Clustering in high-dimensions poses many statistical challenges. While traditional distance-
based clustering methods are computationally feasible, they lack probabilistic interpretation …

Generalized linear models (GLMs) are routinely used for modeling relationships between a
response variable and a set of covariates. The simple form of a GLM comes with easy …

Constrained learning is prevalent in many statistical tasks. Recent work proposes distance-
to-set penalties to derive estimators under general constraints that can be specified as sets …

Broadly, the goal when clustering data is to separate observations into meaningful
subgroups. The rich variety of methods for clustering reflects the fact that the relevant notion …