# MoE theory and gating functions

Area: [Mixture-of-experts](https://mlsanity.com/moe/index.md). HTML: https://mlsanity.com/moe/moe-theory-and-gating-functions/

## Key facts

- MoE theory and gating functions: 188 papers on arXiv since 2017 (data through 14 Oct 2026).
- 23 of its 188 mapped papers (12%) cite “Convergence Rates for Gaussian Mixtures of Experts” (2019, arXiv 1907.04377).
- 20 of its 188 mapped papers (11%) cite “Demystifying Softmax Gating Function in Gaussian Mixture of Experts” (2023, arXiv 2305.03288).
- 14 new papers 17 Sep–14 Oct.

## Most cited here

- [Convergence Rates for Gaussian Mixtures of Experts](https://mlsanity.com/paper/1907.04377/) (2019), cited by 23 papers here
- [Demystifying Softmax Gating Function in Gaussian Mixture of Experts](https://mlsanity.com/paper/2305.03288/) (2023), cited by 20 papers here
- [DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning](https://mlsanity.com/paper/2106.03760/) (2021), cited by 19 papers here
- [A General Theory for Softmax Gating Multinomial Logistic Mixture of Experts](https://mlsanity.com/paper/2310.14188/) (2023), cited by 17 papers here
- [Towards Convergence Rates for Parameter Estimation in Gaussian-gated Mixture of Experts](https://mlsanity.com/paper/2305.07572/) (2023), cited by 13 papers here
- [Statistical Perspective of Top-K Sparse Softmax Gating Mixture of Experts](https://mlsanity.com/paper/2309.13850/) (2023), cited by 12 papers here
- [On Least Square Estimation in Softmax Gating Mixture of Experts](https://mlsanity.com/paper/2402.02952/) (2024), cited by 8 papers here
- [Sigmoid Gating is More Sample Efficient than Softmax Gating in Mixture of Experts](https://mlsanity.com/paper/2405.13997/) (2024), cited by 7 papers here
- [Regularized Estimation and Feature Selection in Mixtures of Generalized Linear Experts](https://mlsanity.com/paper/1907.06994/) (2019), cited by 6 papers here
- [Regularized Maximum Likelihood Estimation and Feature Selection in Mixtures-of-Experts Models](https://mlsanity.com/paper/1810.12161/) (2018), cited by 5 papers here

## New (17 Sep–14 Oct)

- [A Mixture-of-Experts Framework for Multi-Stream Quickest Change Detection](https://mlsanity.com/paper/2610.20876/) (2026-10-14)
- [OrionMoE-v1.0: A Mixture-of-Experts Foundation Model for Tabular In-Context Learning](https://mlsanity.com/paper/2610.20418/) (2026-10-14)
- [LatentLoop: Scaling Looped MoE Models with Asymmetric Compute Allocation](https://mlsanity.com/paper/2610.19975/) (2026-10-14)
- [SLEM: Spectrum-Aware Latent Expert Merging](https://mlsanity.com/paper/2610.16484/) (2026-10-12)
- [Towards Robust Calibration Dataset for Domain-General Mixture of Experts Pruning](https://mlsanity.com/paper/2610.15578/) (2026-10-11)
- [Sparsity Saves Compute, Not Samples: Oracle Inequalities for Dense and Sparse Mixture of Experts](https://mlsanity.com/paper/2610.15039/) (2026-10-10)
- [Cost-Aware Mixture-of-Experts Coordination for Model Markets](https://mlsanity.com/paper/2610.11908/) (2026-10-08)
- [Smoothing the Top-k Exposure Boundary for Sparse Mixture-of-Experts](https://mlsanity.com/paper/2610.11575/) (2026-10-08)
- [Localized Operator Learning with Adaptive Partition-of-Unity Mixture-of-Expert Networks](https://mlsanity.com/paper/2610.04708/) (2026-10-03)
- [Mixture-of-Experts for Cryptocurrency Order Execution: Training Stability, Tail Risk, and Failure Modes](https://mlsanity.com/paper/2610.03369/) (2026-10-02)
- [SPEAR: A Spectral-Disentangled MoE Neural Operator with Knowledge-Guided Expert Aggregation for Large-Scale PDE Pretraining](https://mlsanity.com/paper/2610.03265/) (2026-10-02)
- [Redundancy Meets Synergy: Dependency-aware Expert Selection for MoE via Submodular Optimization](https://mlsanity.com/paper/2610.00558/) (2026-09-30)
- [Scaling Laws for Looped Mixture of Experts](https://mlsanity.com/paper/2609.40316/) (2026-09-30)
- [E-MoE: Enhanced Mixture-of-Experts for Non-Factorized Diffusion Language Models](https://mlsanity.com/paper/2609.37533/) (2026-09-29)