mlsanityData to 14 Oct 2026

Mixture of partially linear experts

arXiv 2405.02905 ↗First posted 5 May 2024stat.ME stat.MLcited by 0

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

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.

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.

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8 of 33 references.

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Same direction, newest first

  1. 2021A non-asymptotic approach for model selection via penalization in high-dimensional mixture of experts models6 Apr

Other references · 7

  1. 2021Scaling Vision with Sparse Mixture of Expertscited by 747
  2. 2019Mixture Models for Diverse Machine Translation: Tricks of the Tradecited by 77
  3. 2013Piecewise regression mixture for simultaneous functional data clustering and optimal segmentationcited by 4
  4. 2016Robust mixture of experts modeling using the t distributioncited by 4
  5. 2017Gaussian Parsimonious Clustering Models with Covariates and a Noise Componentcited by 4
  6. 2018Semiparametric Mixture Regression with Unspecified Error Distributionscited by 1
  7. 2020Mixture of linear experts model for censored data: A novel approach with scale-mixture of normal distributionscited by 1

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  1. 2019Regularized Estimation and Feature Selection in Mixtures of Generalized Linear Experts
  2. 2024On Parameter Estimation in Deviated Gaussian Mixture of Experts
  3. 2021Non-asymptotic model selection in block-diagonal mixture of polynomial experts models
  4. 2020A similarity-based Bayesian mixture-of-experts model
  5. 2017An Introduction to the Practical and Theoretical Aspects of Mixture-of-Experts Modeling
  6. 2024On Least Square Estimation in Softmax Gating Mixture of Experts
  7. 2024Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts
  8. 2022Functional mixture-of-experts for classification