# Mixture of partially linear experts

arXiv: https://arxiv.org/abs/2405.02905 · HTML: https://mlsanity.com/paper/2405.02905/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in MoE theory and gating functions, one of 12 directions in Mixture-of-experts.
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 5 May 2024.

## Placed in

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

## Builds on (most cited first, 8 of 8)

- [Scaling Vision with Sparse Mixture of Experts](https://mlsanity.com/paper/2106.05974/) (2021)
- [Mixture Models for Diverse Machine Translation: Tricks of the Trade](https://mlsanity.com/paper/1902.07816/) (2019)
- [A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models](https://mlsanity.com/paper/2104.02640/) (2021)
- [Piecewise regression mixture for simultaneous functional data clustering and optimal segmentation](https://mlsanity.com/paper/1312.6974/) (2013)
- [Robust mixture of experts modeling using the t distribution](https://mlsanity.com/paper/1701.07429/) (2016)
- [Gaussian Parsimonious Clustering Models with Covariates and a Noise Component](https://mlsanity.com/paper/1711.05632/) (2017)
- [Semiparametric Mixture Regression with Unspecified Error Distributions](https://mlsanity.com/paper/1811.01117/) (2018)
- [Mixture of linear experts model for censored data: A novel approach with scale-mixture of normal distributions](https://mlsanity.com/paper/2007.06635/) (2020)

## Abstract (arXiv)

In the mixture of experts model, a common assumption is the linearity between a response variable and covariates. While this assumption has theoretical and computational benefits, it may lead to suboptimal estimates by overlooking potential nonlinear relationships among the variables. To address this limitation, we propose a partially linear structure that incorporates unspecified functions to capture nonlinear relationships. We establish the identifiability of the proposed model under mild conditions and introduce a practical estimation algorithm. We present the performance of our approach through numerical studies, including simulations and real data analysis.