Improve crps theory slide

index.qmd

@@ -1215,15 +1215,15 @@ EWA satisfies optimal selection convergence \eqref{eq_optp_select} in a determin
 
 <i class="fa fa-fw fa-triangle-exclamation" style="color:var(--col_amber_9);"></i> Learning-rate $\eta$ is chosen correctly
 
-Those results can be converted to *any* stochastic setting @wintenberger2017optimal.
-
 Optimal convex aggregation convergence \eqref{eq_optp_conv} can be satisfied by applying the kernel-trick:
 
 \begin{align}
 \ell^{\nabla}(x,y) = \ell'(\widetilde{X},y) x
 \end{align}
 
-$\ell'$ is the subgradient of $\ell$ at forecast combination $\widetilde{X}$.
+$\ell'$ is the subgradient of $\ell$ at forecast combination $\widetilde{X}$
+
+Those results can be converted to *any* stochastic setting @wintenberger2017optimal
 
 :::