5 papers · 1 filter
Dendrograms of Mixing Measures for Softmax-Gated Gaussian Mixture of Experts: Consistency Without Model Sweeps
Do Tien Hai, Trung Nguyen Mai, TrungTin Nguyen +3
We develop a unified statistical framework for softmax-gated Gaussian mixture of experts (SGMoE) that addresses three long-standing obstacles in parameter estimation and model sele…
The Polynomial Stein Discrepancy for Assessing Moment Convergence
Narayan Srinivasan, Matthew Sutton, Christopher Drovandi +1
We propose a novel method for measuring the discrepancy between a set of samples and a desired posterior distribution for Bayesian inference. Classical methods for assessing sample…
Fast Model Selection and Stable Optimization for Softmax-Gated Multinomial-Logistic Mixture of Experts Models
TrungKhang Tran, TrungTin Nguyen, Md Abul Bashar +3
Mixture-of-Experts (MoE) architectures combine specialized predictors through a learned gate and are effective across regression and classification, but for classification with sof…
Revisiting Incremental Stochastic Majorization-Minimization Algorithms with Applications to Mixture of Experts
TrungKhang Tran, TrungTin Nguyen, Gersende Fort +5
Processing high-volume, streaming data is increasingly common in modern statistics and machine learning, where batch-mode algorithms are often impractical because they require repe…
Model Selection for Gaussian-gated Gaussian Mixture of Experts Using Dendrograms of Mixing Measures
Tuan Thai, TrungTin Nguyen, Dat Do +2
Mixture of Experts (MoE) models constitute a widely utilized class of ensemble learning approaches in statistics and machine learning, known for their flexibility and computational…