# Functional mixture-of-experts for classification

arXiv: https://arxiv.org/abs/2202.13934 · HTML: https://mlsanity.com/paper/2202.13934/

## 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.

## Placed in

- Mixture-of-experts / [MoE theory and gating functions](https://mlsanity.com/moe/moe-theory-and-gating-functions/)

## Builds on (most cited first, 4 of 4)

- [Regularized Estimation and Feature Selection in Mixtures of Generalized Linear Experts](https://mlsanity.com/paper/1907.06994/) (2019)
- [Time series modeling by a regression approach based on a latent process](https://mlsanity.com/paper/1312.6969/) (2013)
- [Joint segmentation of multivariate time series with hidden process regression for human activity recognition](https://mlsanity.com/paper/1312.6956/) (2013)
- [Functional Mixtures-of-Experts](https://mlsanity.com/paper/2202.02249/) (2022)

## Built on by (most cited first, 3 of 3)

- [A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts](https://mlsanity.com/paper/2310.14188/) (2023)
- [Rethinking Multinomial Logistic Mixture of Experts with Sigmoid Gating Function](https://mlsanity.com/paper/2602.01466/) (2026)
- [Fast Model Selection and Stable Optimization for Softmax-Gated Multinomial-Logistic Mixture of Experts Models](https://mlsanity.com/paper/2602.07997/) (2026)

## 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.