Towards a Statistical Understanding of Mixture-of-Experts
Mixture-of-experts (MoE) architectures increase model capacity by combining a collection of expert predictors through input-dependent routing, while often activating only a small subset of experts for each input. Despite their growing importance in modern large-scale models, the statistical roles of their design choices, especially routing, sparse activation, and shared experts, remain only partially understood, as existing theory has largely focused on parametric or correctly specified MoE models. In this paper, we view MoE as a form of localized aggregation and show how this localization reshapes the approximation-estimation-computation tradeoff. We derive oracle risk bounds for learnin...
| Authors | Siyuan He, Bokai Yang, Jie Hu et al. |
|---|---|
| Primary category | stat.ML |
| Published | 3 September 2026 |
| Last updated at source | 3 September 2026 |
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