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 …

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 …

We provide a theoretical treatment of over-specified Gaussian mixtures of experts with
covariate-free gating networks. We establish the convergence rates of the maximum …

We study the maximum likelihood estimation (MLE) in the multivariate deviated model where
the data are generated from the density function $(1-\lambda^{\ast}) h_ {0}(x)+\lambda^{\ast} …

Identifiability of nonparametric mixture models and Bayes optimal clustering Page 1 The
Annals of Statistics 2020, Vol. 48, No. 4, 2277–2302 https://doi.org/10.1214/19-AOS1887 © …

This article considers Bayesian model selection via mean-field (MF) variational
approximation. Towards this goal, we study the non-asymptotic properties of MF inference …

We analyze the posterior contraction rates of parameters in Bayesian models via the
Langevin diffusion process, in particular by controlling moments of the stochastic process …

Uniform consistency in nonparametric mixture models Page 1 The Annals of Statistics 2023,
Vol. 51, No. 1, 362–390 https://doi.org/10.1214/22-AOS2255 © Institute of Mathematical …

Mixtures of regression are useful for regression learning with respect to an uncertain and
heterogeneous response variable of interest. In addition to being a rich predictive model for …