Understanding the parameter estimation of softmax gating Gaussian mixture of experts has
remained a long-standing open problem in the literature. It is mainly due to three …

Is infinity that far? A Bayesian nonparametric perspective of finite mixture models Page 1 The
Annals of Statistics 2022, Vol. 50, No. 5, 2641–2663 https://doi.org/10.1214/22-AOS2201 © …

We consider the problem of testing for a difference in means between clusters of
observations identified via k-means clustering. In this setting, classical hypothesis tests lead …

Estimating the number of components in finite mixture models via the Group-Sort-Fuse
procedure Page 1 The Annals of Statistics 2021, Vol. 49, No. 6, 3043–3069 https://doi.org/10.1214/21-AOS2072 …

Top-K sparse softmax gating mixture of experts has been widely used for scaling up massive
deep-learning architectures without increasing the computational cost. Despite its popularity …

Originally introduced as a neural network for ensemble learning, mixture of experts (MoE)
has recently become a fundamental building block of highly successful modern deep neural …

We revisit the classical problem of deriving convergence rates for the maximum likelihood
estimator (MLE) in finite mixture models. The Wasserstein distance has become a standard …

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 …

Continuously learning to solve unseen tasks with limited experience has been extensively
pursued in meta-learning and continual learning, but with restricted assumptions such as …

Dirichlet Process mixture models (DPMM) in combination with Gaussian kernels have been
an important modeling tool for numerous data domains arising from biological, physical, and …