mlsanityData to 14 Oct 2026

Functional mixture-of-experts for classification

arXiv 2202.13934 ↗First posted 28 Feb 2022stat.ML cs.AI cs.LGcited by 3

On the map: Mixture-of-experts → MoE theory and gating functions

Abstract (arXiv)

We develop a mixtures-of-experts (ME) approach to the multiclass classification where the predictors are univariate functions. It consists of a ME model in which both the gating network and the experts network are constructed upon multinomial logistic activation functions with functional inputs. We perform a regularized maximum likelihood estimation in which the coefficient functions enjoy interpretable sparsity constraints on targeted derivatives. We develop an EM-Lasso like algorithm to compute the regularized MLE and evaluate the proposed approach on simulated and real data.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Placed in MoE theory and gating functions, one of 12 directions in Mixture-of-experts. Builds on 4 of its 18 references that are in the map. First posted on arXiv 28 Feb 2022.

Builds on

4 of 18 references.

Closest ancestors · 2

Same direction, newest first

  1. 2022Functional Mixtures-of-Experts4 Feb
  2. 2019Regularized Estimation and Feature Selection in Mixtures of Generalized Linear Experts14 Jul

Other references · 2

  1. 2013Time series modeling by a regression approach based on a latent processcited by 6
  2. 2013Joint segmentation of multivariate time series with hidden process regression for human activity recognitioncited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 0; 2025: 0; 2026: 2.

3 papers.

  1. 2023A General Theory for Softmax Gating Multinomial Logistic Mixture of Expertscited by 23
  2. 2026Rethinking Multinomial Logistic Mixture of Experts with Sigmoid Gating Functioncited by 0
  3. 2026Fast Model Selection and Stable Optimization for Softmax-Gated Multinomial-Logistic Mixture of Experts Modelscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2019Regularized Estimation and Feature Selection in Mixtures of Gaussian-Gated Experts Models
  2. 2024Learning Mixtures of Experts with EM: A Mirror Descent Perspective
  3. 2018Regularized Maximum Likelihood Estimation and Feature Selection in Mixtures-of-Experts Models
  4. 2026A Heterogeneous Mixture of Experts Framework for Interpretable Machine Learning
  5. 2024Generalization Error Analysis for Sparse Mixture-of-Experts: A Preliminary Study
  6. 2024On Expert Estimation in Hierarchical Mixture of Experts: Beyond Softmax Gating Functions
  7. 2024On Least Square Estimation in Softmax Gating Mixture of Experts
  8. 2026On Bayesian Softmax-Gated Mixture-of-Experts Models