  This work explores the dimension reduction problem for Bayesian nonparametric
regression and density estimation. More precisely, we are interested in
estimating a functional parameter $f$ over the unit ball in $\mathbb{R}^d$,
which depends only on a $d_0$-dimensional subspace of $\mathbb{R}^d$, with $d_0
< d$.It is well-known that rescaled Gaussian process priors over the function
space achieve smoothness adaptation and posterior contraction with near
minimax-optimal rates. Moreover, hierarchical extensions of this approach,
equipped with subspace projection, can also adapt to the intrinsic dimension
$d_0$ (\cite{Tokdar2011DimensionAdapt}).When the ambient dimension $d$ does not
vary with $n$, the minimax rate remains of the order $n^{-\beta/(2\beta
+d_0)}$.%When $d$ does not vary with $n$, the order of the minimax rate remains
the same regardless of the ambient dimension $d$. However, this is up to
multiplicative constants that can become prohibitively large when $d$ grows.
The dependences between the contraction rate and the ambient dimension have not
been fully explored yet and this work provides a first insight: we let the
dimension $d$ grow with $n$ and, by combining the arguments of
\cite{Tokdar2011DimensionAdapt} and \cite{Jiang2021VariableSelection}, we
derive a growth rate for $d$ that still leads to posterior consistency with
minimax rate.The optimality of this growth rate is then discussed.Additionally,
we provide a set of assumptions under which consistent estimation of $f$ leads
to a correct estimation of the subspace projection, assuming that $d_0$ is
known.
