An $l_1$-oracle inequality for the Lasso in mixture-of-experts regression models. (arXiv:2009.10622v3 [math.ST] UPDATED)

Mixture-of-experts (MoE) models are a popular framework for modeling
heterogeneity in data, for both regression and classification problems in
statistics and machine learning, due to their flexibility and the abundance of
available statistical estimation and model choice tools. Such flexibility comes
from allowing the mixture weights (or gating functions) in the MoE model to
depend on the explanatory variables, along with the experts (or component
densities). This permits the modeling of data arising from more complex data
generating processes when …

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