Base Boosting
Base Boosting
Base boosting is a generalization of gradient boosting, which fits a hybrid additive and varying coefficient model. 

- Namely, gradient boosting fits an additive model:
\begin{equation}
h(X ; \{ \alpha, \theta\}) = \alpha_{0} + \sum_{k=1}^{K} \alpha_{k} b(X ; \theta_{k}),
\end{equation}
where the boosting mechanism begins optimization in function space at a constant model.

- In contrast, base boosting fits the hybrid additive and varying coefficient model:
\begin{equation}
h(X ; \{ \alpha, \theta\}) = \gamma(X) + \sum_{k=1}^{K} \alpha_{k} b(X ; \theta_{k}),
\end{equation}
where the boosting mechanism begins optimization in function space at a base model, which may be a non-constant model.

A special case is the coordinate functional:
\begin{equation}
\gamma(X) = \pi_{j}(X) = X_{j}
\end{equation}
where $X_{j}$ denotes a prediction generated by the base model.

- This setup facilitates knowledge transfer between the base model and boosting mechanism.
Boosting on the shoulders of giants in quantum device calibration
https://paperswithcode.com/paper/boosting-on-the-shoulders-of-giants-in
Boosting on the shoulders of giants in quantum device calibration