diff --git a/index.html b/index.html
index 10d884c..156e7b2 100644
--- a/index.html
+++ b/index.html
@@ -26298,12 +26298,12 @@ w_{t,k}^{\text{Naive}} = \frac{1}{K}\label{eq:naive_combination}
EWA satisfies optimal selection convergence \(\eqref{eq_optp_select}\) in a deterministic setting if:
Loss \(\ell\) is exp-concave
Learning-rate \(\eta\) is chosen correctly
-Those results can be converted to any stochastic setting Wintenberger (2017).
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 Wintenberger (2017)
diff --git a/index.qmd b/index.qmd
index 382890a..068588c 100644
--- a/index.qmd
+++ b/index.qmd
@@ -1215,15 +1215,15 @@ EWA satisfies optimal selection convergence \eqref{eq_optp_select} in a determin
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
:::