Blessing of Dimensionality for Approximating Sobolev Classes on Manifolds

## Abstract

Abstract The manifold hypothesis says that natural high-dimensional data is actually supported on or around a low-dimensional manifold. Recent success of statistical and learning-based methods empirically supports this hypothesis, due to outperforming classical statistical intuition in very high dimensions. A natural step for analysis is thus to assume the manifold hypothesis and derive bounds that are independent of any embedding space. Theoretical implications in this direction have recently been explored in terms of generalization of ReLU networks and convergence of Langevin methods. We complement existing results by providing theoretical statistical complexity results, which directly relates to generalization properties. In particular, we demonstrate that the statistical complexity required to approximate a class of bounded Sobolev functions on a compact manifold is bounded from below, and moreover that this bound is dependent only on the intrinsic properties of the manifold. These provide complementary bounds for existing approximation results for ReLU networks on manifolds, which give upper bounds on generalization capacity.

## 1 Introduction

Data is ever growing, especially in the current era of machine learning. However, dimensionality is not always beneficial, and having too many features can confound simpler underlying truths. This is sometimes referred to as the curse of dimensionality (Altman & Krzywinski, ). A classical example is manifold learning, which is known to scale exponentially in the intrinsic dimension (Narayanan & Niyogi, ). In the current paradigm of increasing dimensionality, standard statistical tools and machine learning models continue to work, despite the high latent dimensions arising in cases such as computational imaging (Wainwright, ). One possible assumption to elucidate this phenomenon comes from the manifold hypothesis, also known as concentration of measure or the blessing of dimensionality (Bengio et al., ). This states that real datasets are actually concentrated on or near low-dimensional manifolds, independently of the latent dimension that the data is embedded in.

The manifold hypothesis is generally considered as a theoretical assumption, with empirical studies considering the dimension rather than the construction of the manifold. However, more concrete examples of structure in data have also been considered and exploited (Celledoni et al., ). Group equivariance describes how an operation commutes with symmetries in data, such as rotation or reflections in image reconstruction, with applications in unsupervised learning and enforcing faithful neural network reconstructions (Chen et al., ; Cohen & Welling, ).

Structures can also be found and exploited outside of raw data. Algorithmic structures such as proximal splitting algorithms, arising from convex optimization, are used in Plug-and-Play image reconstruction algorithms (Kamilov et al., ; Venkatakrishnan et al., ). Learning-to-optimize considers pushing the underlying geometry of the data into corresponding optimization problems, and exploiting common geometry in the function space to achieve significant acceleration in a learned fashion (Andrychowicz et al., ; Tan et al., ).

In this work, we explore the consequences of the manifold hypothesis through the lens of approximation theory and statistical complexity. For a class of functions with infinite statistical complexity, we consider a nonlinear width in terms of how well it can be approximated in $L^{p}$ with function classes of finite statistical complexity. This characterizes how close the function class is to having low statistical complexity, motivated by having good sample complexity to learn nearly-optimal functions. We consider how difficult it is to optimally approximate classes of functions with functions of finite statistical complexity in terms of $L^{p}$ distance. In particular, Theorem 3.1 demonstrates that on a Riemannian manifold, the optimal approximation of a bounded Sobolev class with function classes of finite pseudo-dimension has a rate depending only on the *implicit* properties of the manifold.

### 1.1 Related Literature

We detail some literature surrounding the manifold hypothesis, including empirical testing, as well as other theoretical works that show rates that depend mainly on intrinsic properties of the underlying manifold. We note that the manifold hypothesis is sometimes replaced with the “union of manifolds” hypothesis, where the component manifolds are allowed to have different intrinsic dimension (Vidal, ; Brown et al., ). We will consider only the case where the manifold has constant intrinsic dimension, but do not assume connectedness(^1^11In particular, for disconnected manifolds, our results can be applied to each (compact) connected component.) for our results.

Intrinsic dimension estimation. Methods for empirically testing the manifold hypothesis typically involve assuming the samples follow some statistical process, where the dimension parameter is then estimated from samples using maximum likelihood estimation of distances between points (Pope et al., ; Block et al., ; Levina & Bickel, ). Throughout, we will consider only Riemannian manifolds that are compact and without boundary. This is standard in the literature, and allows for nontrivial concepts such as injectivity radius, diameters and curvature bounds.

Learning the manifold/dimension reduction. In modern datasets, a reasonable proxy to the image manifold hypothesis is to have a representation of the low-dimensional structure, constructed from finitely many samples. Fefferman et al. () provides a classical algorithm to test whether a set of points can be described by a manifold with sufficient regularity properties. Classical methods to find nonlinear low-dimensional manifolds include locally linear embedding (Roweis & Saul, ), Isomap (Tenenbaum et al., ), eigenmaps (Belkin & Niyogi, ), and topological properties (Niyogi et al., ; ), with more comprehensive reviews given in (Lee et al., ). A more modern approach uses generative networks by using the latent space as the manifold, which bypasses using the intrinsic dimension itself as an algorithmic parameter (Wang et al., ; DeMers & Cottrell, ; Nakada & Imaizumi, ).

Manifold-driven architectures. Driven by the manifold hypothesis, several machine learning approaches consider enforcing a network output to be low-dimensional. Common examples are variational auto-encoders, which consist of an “encoder” network mapping from the input to a latent space, and “decoder” network mapping from the latent space to an output (Kingma & Welling, ; Connor et al., ). By restricting the dimensionality of the latent space, the output manifold will automatically be restricted. Other methods include bottleneck layers in ResNets, which relate to the information bottleneck tradeoff between compression and prediction (Tishby & Zaslavsky, ; Shwartz-Ziv & Tishby, ).

Approximation capacity of ReLU networks. Chen et al. () provide approximation rates of ReLU networks for Hölder functions on manifolds based on the width, depth and total parameters, albeit still depending linearly on the latent dimension of the model, assuming isometric embedding in Euclidean space. They provide approximations based on partitions of unity and classical constructions on near-Euclidean charts. The same authors provide associated empirical risk estimates and generalization bounds for ReLU networks in (Chen et al., ). Labate & Shi () consider uniform generalization of the class of ReLU networks for Hölder functions on the manifold, using the Johnson-Lindenstrauss lemma to work in near-isometry to Euclidean space. Yang et al. () addresses the complexity of approximating a Sobolev function on the unit hypercube constructively with ReLU DNNs by showing an upper bound on the Vapnik-Chervonenkis (VC) dimension and pseudo-dimension of derivatives of neural networks based on the number of layers, input dimension, and maximum width.

Langevin mixing times. For an isometrically embedded manifold, Block et al. () bounds a log-Sobolev constant for probability measures supported on the manifold that are absolutely continuous with respect to the volume measure, mollified with Gaussian densities in the ambient space. Wang et al. () demonstrate linear convergence of the Kullback-Leibler divergence with rates depending only on the intrinsic dimension for the geodesic Langevin algorithm, which incorporates the Riemannian metric into the noise in the unadjusted Langevin algorithm.

Complexity lower bounds. Bubeck & Sellke () shows that for a class of Lipschitz functions interpolating a noisy set of samples, if the ERM is below the noise level, then the Lipschitz constant of $f$ scales as $\sqrt{nD/p}$, where $n$ is the number of samples, $D$ is the latent dimension, and $p$ is the number of parameters. Gao et al. () lower-bounds the VC dimension of any class of functions that can robustly interpolate $n$ samples as $\Omega(nD)$, also demonstrating a strict computational increase required for robust learning.

In Section 2, we formally introduce the concepts of pseudo-dimension and desired notion of the width of a function class, followed by some existing results relating complexity to generalization behavior. We also briefly discuss Riemannian manifolds and prerequisite knowledge needed for the main results. The main result is Theorem 3.1, with proof given in Section 3.

## 2 Background

This section will introduce the necessary concepts of statistical complexity, Riemannian geometry, and Sobolev functions on manifolds. We reduce the technicalities throughout and refer to the references for more detailed expositions.

### 2.1 Pseudo-Dimension as Complexity

We consider a concept of statistical complexity called the pseudo-dimension (Pollard, ; Anthony & Bartlett, ). This extends the classical concept of Vapnik-Chervonenkis (VC) dimension from indicator-valued to real-valued functions.

###### Definition 2.1 .

Let ${\mathcal{H}}$ be a class of real-valued functions with domain ${\mathcal{X}}$.
Let $X_{n}=\{x_{1},...,x_{n}\}\subset{\mathcal{X}}$, and consider a collection of reals $s_{1},...,s_{n}\subset\mathbb{R}^{n}$. When evaluated at each $x_{i}$, a function $h\in{\mathcal{H}}$ will lie on one side(^2^22We adopt the notation of $\operatorname{sign}(0)=+1$ for well-definedness, but the other option is equally valid.) of the corresponding $x_{i}$, i.e. $\operatorname{sign}(h(x_{i})-s_{i})=\pm 1$. The vector of such sides $(\operatorname{sign}(h(x_{i})-s_{i}))_{i=1}^{n}$ is thus an element of $\{\pm 1\}^{n}$.

We say that ${\mathcal{H}}$ *P-shatters* $X_{n}$ if there exist reals $s_{1},...,s_{n}$ such that all possible sign combinations are obtained, i.e.,

$$ $\{(\operatorname{sign}(h(x_{i})-s_{i}))_{i}\mid h\in{\mathcal{H}}\}=\{\pm 1\}^ {n}.$ $$

The *pseudo-dimension* $\dim_{p}({\mathcal{H}})$ is the cardinality of the largest set that is P-shattered:

$$ $\dim_{p}({\mathcal{H}})=\sup\left\{n\in\mathbb{N}\mid\exists\{x_{1},...,x_{n} \}\subset{\mathcal{X}}\text{ that is P-shattered by }{\mathcal{H}}\right\}.$ (1) $$

We note that the classical definition of the VC dimension takes a similar form, but without the biases $s_{i}$ and with ${\mathcal{H}}$ being a class of binary functions taking values in $\{\pm 1\}$. The pseudo-dimension satisfies similar properties as the VC dimension, such as coinciding with the standard notion of dimension for vector spaces of functions.

Figure: Figure 1: For the class of affine 1D functions $\{x\mapsto ax+b\mid a,b\in\mathbb{R}\}$, this choice of $\{x_{1},x_{2}\}\subset\mathbb{R}$ and $s_{1},s_{2}\in\mathbb{R}$ on the left is P-shattered. We exhibit affine functions $f_{\pm\pm}$ that take all possible combinations of being above/below $s_{i}$ at the $x_{i}$. However, there is no possible arrangement of three points that is P-shattered by affine functions. For example, the arrangement on the right would not have a function that goes below the left and right points but above the middle point. Therefore, the pseudo-dimension of the class of affine functions is 2.
Refer to caption: x1.png

###### Proposition 2.2 ( Anthony & Bartlett 1999 , Thm. 11.4 ) .

If ${\mathcal{H}}^{\prime}$ is an $\mathbb{R}$-vector space of real-valued functions, then $\dim_{P}({\mathcal{H}}^{\prime})=\dim({\mathcal{H}}^{\prime})$ as a vector space. In particular, if ${\mathcal{H}}$ is a subset of a vector space ${\mathcal{H}}^{\prime}$ of real-valued functions, then $\dim_{P}({\mathcal{H}})\leq\dim({\mathcal{H}}^{\prime})$.

Much like the VC dimension and other statistical complexity quantities such as Rademacher complexity or Gaussian complexity, low complexity leads to better generalization properties of empirical risk minimizers (Bartlett & Mendelson, ). One example is as follows, where precise definitions can be found in Appendix A. Informally, the $(\epsilon,\delta)$-sample complexity measures the generalization performance of an approximate empirical risk minimizer, given by the number of samples such that an approximate empirical risk minimizer has true risk at most $\epsilon$ above the optimum with probability $1-\delta$.

###### Proposition 2.3 ( Anthony & Bartlett 1999 , Thm. 19.2 ) .

Let ${\mathcal{H}}$ be a class of functions mapping from a domain $X$ into $[0,1]\subset\mathbb{R}$, and that ${\mathcal{H}}$ has finite pseudo-dimension. Then the $(\epsilon,\delta)$-sample complexity is bounded by

$$ $m_{L}(\epsilon,\delta)\leq\frac{128}{\epsilon^{2}}\left(2\dim_{P}({\mathcal{H} })\log\left(\frac{34}{\epsilon}\right)+\log\left(\frac{16}{\delta}\right) \right).$ (2) $$

To compare the approximation of one function class by another, we consider a nonlinear width induced by a normed space.

###### Definition 2.4 .

Let ${\mathcal{F}}$ be a normed space of functions. Given two subsets $F_{1},F_{2}\subset{\mathcal{F}}$, the *(asymmetric) Hausdorff distance* between the two subsets is the largest distance of an element of $F_{1}$ to its closest element in $F_{2}$:

$$ $\operatorname{dist}(F_{1},F_{2};{\mathcal{F}})=\sup_{f_{1}\in F_{1}}\inf_{f_{2 }\in F_{2}}\|f_{1}-f_{2}\|_{{\mathcal{F}}}.$ (3) $$

For a subset $F\subset{\mathcal{F}}$, the *nonlinear $n$-width* is given by the optimal (asymmetric) Hausdorff distance between $F$ and ${\mathcal{H}}^{n}$, infimized over classes ${\mathcal{H}}^{n}$ in ${\mathcal{F}}$ with $\dim_{p}({\mathcal{H}}^{n})\leq n$:

$$ $\rho_{n}(F,{\mathcal{F}})=\inf_{{\mathcal{H}}^{n}}\operatorname{dist}(F,{ \mathcal{H}}^{n};{\mathcal{F}})=\inf_{{\mathcal{H}}^{n}}\sup_{f\in F}\inf_{h \in{\mathcal{H}}^{n}}\|f-h\|_{\mathcal{F}}.$ (4) $$

This width measures the complexity in terms of how closely the entire function class can be approximated with another class of finite pseudo-dimension. In Section 3, we provide a lower bound on the nonlinear $n$-width of a bounded Sobolev class of functions. In terms of neural network approximation, these lower bounds complement existing approximation results of ReLU networks, which effectively provide an upper bound on the width by using the class of (bounded width, layers and parameters) ReLU networks as the finite pseudo-dimension approximating class.

###### Definition 2.1 .

###### Proposition 2.2 ( Anthony & Bartlett 1999 , Thm. 11.4 ) .

###### Proposition 2.3 ( Anthony & Bartlett 1999 , Thm. 19.2 ) .

###### Definition 2.4 .

### 2.2 Riemannian Geometry

We begin with some basic definitions, as well as recall the Bishop-Gromov comparison theorem, a volume argument that will be used throughout the next section. We give informal definitions of the sectional and Ricci curvature, and refer to Bishop & Crittenden (); Gallot et al. () for a more detailed exposition.

###### Definition 2.5 .

A $d$-dimensional *Riemannian manifold* is a real smooth manifold $M$ equipped with a Riemannian metric $g$, which defines an inner product on the tangent plane $T_{p}M$ at each point $p\in M$. We assume $g$ is smooth, i.e. for any smooth chart $(U,x)$ on $M$, the components $g^{ij}=g(\frac{\partial}{\partial x_{i}},\frac{\partial}{\partial x_{j}}):U
\rightarrow\mathbb{R}$ are $\mathcal{C}^{\infty}$.

A manifold is without boundary if every point has a neighborhood homeomorphic to an open subset of $\mathbb{R}^{d}$.

The *sectional (or Riemannian) curvature* takes at each point $p\in M$, a tangent plane $P\subset T_{p}M$ and outputs a scalar value. The *Ricci curvature* of a unit vector $e\in T_{p}M$ is the mean sectional curvature over planes containing a given unit vector in $T_{p}M$. The *scalar curvature* is the trace of the Ricci curvature.

The *injectivity radius* $\operatorname{inj}(p)$ at a point $p\in M$ is the supremum over radii $r>0$ such that the exponential map defines a global diffeomorphism (nonsingular derivative) from $B_{r}(0;T_{p}M)$ onto its image in $M$. The injectivity radius $\operatorname{inj}(M)$ of a manifold is the infimum of such injectivity radii over all points in $M$.

A Riemannian manifold has a (unique) natural volume form, denoted $\mathrm{vol}_{M}$. In local coordinates, the volume form is

$$ $\mathrm{vol}_{M}=\sqrt{|g|}\mathop{}\!\mathrm{d}x_{1}\wedge...\wedge\mathop{} \!\mathrm{d}x_{d},$ (5) $$

where $g$ is the Riemannian metric, and $\mathop{}\!\mathrm{d}x_{1},...,\mathop{}\!\mathrm{d}x_{d}$ is a (positively-oriented) cotangent basis.

Out of the three concepts of curvature, the sectional curvature is the most descriptive, and a complete understanding of the sectional curvature gives Riemannian geometry. In particular, for a manifold with constant sectional curvature $K$, we have $\mathrm{Ric}=(d-1)Kg$ and $\textrm{Scal}=d(d-1)K$.

Intuitively, the Ricci curvature controls the behavior of geodesics that are close. For manifolds of positive Ricci curvature, such as on a sphere, geodesics tend to converge. In manifolds with negative Ricci curvature, such as hyperbolic space, geodesics tend to diverge.

Within the ball of injectivity, geodesics are minimizing curves. The injectivity radius defines the largest ball on which the geodesic normal coordinates may be used, where it locally behaves as $\mathbb{R}^{d}$. This is an intrinsic quantity of the manifold, which does not depend on the embedding.

The volume form can be thought of as a higher-dimensional surface area, where the scaling term $\sqrt{|g|}$ arises from curvature and choice of coordinates. For example, for the 2-sphere $\mathbb{S}^{2}$ embedded in $\mathbb{R}^{3}$, the volume form is simply the surface measure, which can be expressed in terms of polar coordinates. We drop the subscripts when taking the volume of the whole manifold $\mathrm{vol}(M)=\mathrm{vol}_{M}(M)$.

Throughout, we will assume that our Riemannian manifold is complete, compact, without boundary, and connected. We note that the connectedness assumption can be dropped by working instead on each connected component, since the arguments will be intrinsic and do not depend on any embeddings. We briefly state some properties of compact Riemannian manifolds.

###### Proposition 2.6 .

Let $(M,g)$ be a compact Riemannian manifold without boundary. The following statements hold.

- 1.
(Bounded curvature) The sectional curvature (and hence the Ricci curvature) is uniformly bounded from above and below (Bishop & Crittenden, , Sec. 9.3).
- 2.
(Positive injectivity radius) Let the sectional curvature $K_{m}$ be bounded by some $|K_{M}|\leq K$. Suppose there exists a point $p\in M$ and constant $v_{0}>0$ such that $\mathrm{vol}_{M}(B_{1}(p))\geq v_{0}$. Then there exists a positive constant $i_{1}=i_{1}(K,v_{0},d)$ such that (Cheeger et al., ; ):
$\operatorname{inj}(p)\geq i_{1}>0$
In particular, since $M$ is compact, using a finite covering argument, $\operatorname{inj}(M)$ is bounded below by some positive constant.

We now state the celebrated Bishop-Gromov theorem (Petersen, ; Bishop, ). This is an essential volume-comparison theorem.

###### Theorem 2.7 (Bishop-Gromov) .

Let $(M,g)$ be a complete $d$-dimensional Riemannian manifold whose Ricci curvature is bounded below by $\mathrm{Ric}\geq(d-1)K$, for some $K\in\mathbb{R}$. Let $M^{d}_{K}$ be the complete $d$-dimensional simply connected space of constant sectional curvature $K$, i.e. a $d$-sphere of radius $1/\sqrt{K}$, $d$-dimensional Euclidean space, or scaled hyperbolic space if $K>0$, $K=0$, $K<0$ respectively. Then for any $p\in M$ and $p_{K}\in M_{K}^{n}$, we have that

$$ $\phi(r)=\mathrm{vol}_{M}(B_{r}(p))/\mathrm{vol}_{M^{d}_{K}}(B_{r}(p_{K}))$ (6) $$

is non-increasing on $(0,\infty)$. In particular, $\mathrm{vol}_{M}(B_{r}(p))\leq\mathrm{vol}_{M^{d}_{K}}(B_{r}(p_{K}))$.

A simple corollary that uses the volume of a hyperbolic ball to specify the rate of change in closed form is given as follows (Block et al., ; Ohta, ). We write $\mathrm{Ric}\geq K$ for $K\in\mathbb{R}$ to mean that $\mathrm{Ric}(v)\geq K$ holds for all unit vectors in the tangent bundle $v\in TM$.

###### Corollary 2.8 .

Let $(M,g)$ be a $d$-dimensional Riemannian manifold such that $\mathrm{Ric}\geq-(d-1)K$ for some $K>0$. For any point $x\in M$, let $B_{r}(x)$ be the metric ball around $x$ in $M$ with radius $r>0$. For any $0<r<R$, we have

$$ $\frac{\mathrm{vol}_{M}(B_{R}(x))}{\mathrm{vol}_{M}(B_{r}(x))}\leq\frac{\int_{0 }^{R}s^{d-1}}{\int_{0}^{r}s^{d-1}},\quad s(u)=\sinh(u\sqrt{K}).$ (7) $$

###### Definition 2.5 .

###### Proposition 2.6 .

###### Theorem 2.7 (Bishop-Gromov) .

###### Corollary 2.8 .

### 2.3 Sobolev Functions on Manifolds

Equipped with curvature results on the manifold, we need to define the desired function class that we wish to bound. We define Sobolev spaces in a relatively informal manner, and restrict the exposition to compact Riemannian manifolds. Note that there are many different ways to define the Sobolev spaces on manifolds due to the curvature, and we consider the variant presented in Hebey ().

###### Definition 2.9 ( Hebey 2000 , Sec. 2.2 ) .

Let $(M,g)$ be a smooth Riemannian manifold. For integer $k$, $p\geq 1$, and smooth $u:M\rightarrow\mathbb{R}$, define by $\nabla^{k}u$ the $k$’th covariant derivative of $u$, and $|\nabla^{k}u|$ the norm, defined in a local chart(^3^33Recall that in a chart $(U,x)$, $g^{ij}=g(\partial/\partial x_{i},\partial/\partial x_{j})$.) as

$$ $|\nabla^{k}u|^{2}=g^{i_{1}j_{1}}...g^{i_{k}j_{k}}(\nabla^{k}u)_{i_{1}...i_{k}} (\nabla^{k}u)_{j_{1}...j_{k}},$ (8) $$

using Einstein’s summation convention where repeated indices are summed. Define the set of admissible test functions (with respect to the volume measure) as

$$ $\mathcal{C}^{k,p}(M)\coloneqq\left\{u\in\mathcal{C}^{\infty}(M)\mid\forall j=0 ,...,k,\,\int_{M}|\nabla^{j}u|^{p}\mathop{}\!\mathrm{d}\mathrm{vol}(g)<+\infty \right\},$ (9) $$

and for $u\in\mathcal{C}^{k,p}(M)$, the Sobolev norm

$$ $\|u\|_{H^{k,p}}\coloneqq\sum_{j=0}^{k}\left(\int_{M}|\nabla^{j}u|^{p}\mathop{} \!\mathrm{d}\mathrm{vol}(g)\right)^{1/p}$ (10) $$

The Sobolev spaces are defined as follows, where $\alpha$ is a $d$-multi-index:

$$ $\displaystyle H^{k,p}(M)$ $\displaystyle=\text{completion of }\mathcal{C}^{k,p}\text{ under }\|\cdot\|_{H ^{k,p}}$ (11) $\displaystyle W^{k,p}(M)$ $\displaystyle=\{u\in L^{p}(M)\mid\forall|\alpha|\leq k,\,D_{\alpha}u\in L^{p}( M)\}.$ $$

It can be shown that Sobolev functions on a compact Riemannian manifold share similar embedding properties as in Euclidean space. Moreover, the weak Sobolev space $W^{k,p}(M,g)$ is the same as $H^{k,p}$, the completion of all $\mathcal{C}^{\infty}$ functions with $L^{p}(M)$-integrable derivatives up to order $k$ with respect to the Sobolev norm (Adams & Fournier, ; Meyers & Serrin, ). We briefly mention the manifold versions of the embedding theorems and Morrey’s inequality, which embed into $L^{p}$ spaces and Hölder spaces respectively. Other corresponding inclusions such as Rellich-Kondrachov and Sobolev-Poincaré also continue to hold, and we refer to Hebey () for a more detailed treatment.

We adopt the following definition of a bounded Sobolev ball. This is a natural extension of a $L^{p}$-ball to Sobolev spaces and provides a compact space of functions to approximate.

###### Definition 2.10 .

For constant $C\geq 0$, the bounded Sobolev ball $W^{k,p}(C;M)$ is given by the set of all functions with weak derivatives bounded in $L^{p}$ by $C$:

$$ $W^{k,p}(C;M)=\left\{u\in W^{k,p}(M)\mid\forall l\leq k,\,\|\nabla^{l}u\|_{p} \leq C\right\}$ (12) $$

We write $W^{k,p}(C)$ to mean $W^{k,p}(C;M)$ for ease of notation.

We note that it is possible to have other definitions of bounded Sobolev functions, such as bounding only the $L^{p}$-norm of one multi-indexed derivative. However, this will only change the bounds up to some constant. In the following section, we derive a lower bound on the nonlinear $n$-width of bounded Sobolev balls $W^{k,p}(1)$ in $L^{q}$.

###### Definition 2.9 ( Hebey 2000 , Sec. 2.2 ) .

###### Definition 2.10 .

## 3 Main Result

This section begins with a statement of the main approximation result, followed by high-level intuition behind the proof. We then state several lemmas that will be useful, and finish with the proof.

### 3.1 Statement

###### Theorem 3.1 .

Let $(M,g)$ be a $d$-dimensional compact (separable) Riemannian manifold without boundary. From compactness, there exist real constants such that:

- 1.
The Ricci curvature satisfies $\mathrm{Ric}\geq-(d-1)K$, where $K>0$;
- 2.
$\operatorname{inj}(M)>0$ is the injectivity radius;

Moreover, for any $1\leq p,q\leq+\infty$, the nonlinear width of $W^{1,p}(1)$ satisfies the lower bound for sufficiently large $n$:

$$ $\rho_{n}(W^{1,p}(1),L^{q}(M,\mathrm{vol}_{M}))\geq C(d,K,\mathrm{vol}(M),p,q)( n+\log n)^{-1/d}.$ (13) $$

In particular, the constant is independent of any latent dimension that $(M,g)$ may be embedded in.

We note that since the manifold Sobolev space is defined directly on the manifold, embeddings are not necessary, which removes any dependence on latent dimension. This theorem should be contrasted with Maiorov & Ratsaby (, Thm. 1), which produces a similar bound for the bounded Sobolev space on $[0,1]^{d}$, but with lower bound $n^{-1/d}$. The additional $\log n$ term is necessary due to the curvature of the space. We also note that it is possible to perform this analysis in the case of positive curvature.

There are two major differences in converting the proof of Maiorov & Ratsaby () to the manifold setting. Firstly, Maiorov & Ratsaby () uses a partition into hypercubes to construct the desired counterexample. As such hypercube partitions generally do not pose nice properties on manifolds, this must be loosened to a packing of geodesic balls, which does not fully cover the manifold and loosens the bound. The second major difference is the presence of curvature and its non-locality, where geodesic balls of the same radius can have drastically different volumes, even if it is pointwise asymptotically Euclidean. This will introduce additional constants into the final bound.

###### Theorem 3.1 .

### 3.2 Proof Sketch

The proof follows a the ideas for a similar statement in Maiorov & Ratsaby () for the cube $[0,1]^{d}$, with slight corrections and explicit constant tracking. The major differences arise due to the curvature, where small-ball volumes are pointwise asymptotically Euclidean, but not uniformly over the entire manifold. We can break down the proof into several steps.

- Step 1.
We consider a class of simple functions, defined as sums of cutoff functions with disjoint supports. The class of simple functions is such that the $L^{1}$-norm of each component is large within the class of bounded $W^{1,p}(1)$ functions.
- Step 2.
An appropriate subset of the simple functions is then taken, that is isometric to the $\{\pm 1\}^{m}$-hypercube. Since the vertices of the hypercube are $\ell_{1}$-well separated, there exists an $L_{1}$-well-separated subset of our class of functions.
- Step 3.
We then show that it is not possible to bypass the given bound by trying to approximate the constructed set of well-separated functions with a low-pseudodimension class. This step uses an exponential lower bound on the metric entropy and a polynomial upper bound from Bishop-Gromov to derive a contradiction.

#### 3.2.1 Packing Lemmas

We list a few lemmas that will be useful. Proofs are given in Appendix B for completeness. We use the following definition of covering number.

###### Definition 3.2 (Covering number) .

For a metric space $(M,d)$ and radius $\varepsilon>0$, the *covering number* $N_{\varepsilon}(M)$ is the maximum number of points $x_{1},...,x_{n}\in M$ such that the open balls $B_{\varepsilon}(x_{i})$ are disjoint. The *metric entropy* $\mathcal{M}_{\varepsilon}$ is the maximum number of points $x_{1},...,x_{m}\in M$ such that $d(x_{i},x_{j})\geq\varepsilon$ for $i\neq j$.

###### Remark 3.3 .

The following inequality holds:

$$ $\mathcal{M}_{2\varepsilon}\leq N_{\varepsilon}\leq\mathcal{M}_{\varepsilon}.$ (14) $$

The first inequality holds since any $2\varepsilon$-separated subset has disjoint $\varepsilon$-balls. The second inequality holds since any set with disjoint $\varepsilon$-balls is necessarily $\epsilon$-separated.

###### Proposition 3.4 (Packing number estimates) .

Suppose $(M,g)$ has curvature lower-bounded by $K\in\mathbb{R}$, diameter $D$ and dimension $d$. Let $M^{d}_{K}$ be the $d$-dimensional model space of constant sectional curvature $K$ (i.e. sphere, Euclidean space, or hyperbolic space). The packing number $N_{\varepsilon}(M)$ satisfies, where $p_{K}$ is any point in $M^{d}_{K}$:

$$ $\frac{\mathrm{vol}(M)}{\mathrm{vol}_{M^{d}_{K}}(B_{2\varepsilon}(p_{K}))}\leq N _{\varepsilon}\leq\frac{\mathrm{vol}_{M^{d}_{K}}(B_{D}(p_{K}))}{\mathrm{vol}_{ M^{d}_{K}}(B_{\varepsilon}(p_{K}))}$ (15) $$

###### Proof.

Uses Bishop-Gromov. Deferred to Proposition B.1.
∎

We note that the volume of a ball of given radius in space of constant curvature is independent of the given point. We drop the where necessary, and write

$$ $\mathrm{vol}_{M^{d}_{K}}(B_{r})\coloneqq\mathrm{vol}_{M^{d}_{K}}(B_{r}(p_{K}))$ $$

to denote the volume of any ball of radius $r$ in $M^{d}_{K}$. We finish the required expositions with a bound on the metric entropy for bounded functions.

###### Lemma 3.5 ( Haussler 1995 , Cor. 2 and 3 ) .

For any set $X$, any probability distribution $P$ on $X$, any distribution $Q$ on $\mathbb{R}$, any set ${\mathcal{F}}$ of $P$-measurable real-valued functions on $X$ with $\dim_{P}({\mathcal{F}})=n<\infty$ and any $\varepsilon>0$, the metric entropy ${\mathcal{M}}_{\varepsilon}$ (largest cardinality of a $\varepsilon$-separated subset, where distance between any two elements is $\geq\varepsilon$) satisfies:

$$ ${\mathcal{M}}_{\varepsilon}({\mathcal{F}},\sigma_{P,Q})\leq e(n+1)\left(\frac{ 2e}{\varepsilon}\right)^{n}.$ (16) $$

Specifically, taking $L^{1}$ distance, if $\mathcal{F}$ is $P$-measurable taking values in the interval $[0,1]$, we have

$$ ${\mathcal{M}}_{\varepsilon}({\mathcal{F}},L^{1}(P))\leq e(n+1)\left(\frac{2e}{ \varepsilon}\right)^{n}.$ (17) $$

If $\sigma$ is instead a finite measure, and $\mathcal{F}$ is $\sigma$-measurable taking values in the interval $[-\beta,\beta]$, then

$$ ${\mathcal{M}}_{\varepsilon}({\mathcal{F}},L^{1}(\sigma))\leq e(n+1)\left(\frac {4e\beta\sigma(X)}{\varepsilon}\right)^{n}.$ (18) $$

###### Remark 3.6 .

The final inequality comes from the second-to-last inequality, by noting that a $\varepsilon$-separated set in $\sigma$ corresponds to a $\varepsilon/\sigma(X)$-separated set in the normalized measure $\sigma/\sigma(X)$, as well as scaling everything by $2\beta$.

###### Definition 3.2 (Covering number) .

###### Remark 3.3 .

###### Proposition 3.4 (Packing number estimates) .

###### Proof.

###### Lemma 3.5 ( Haussler 1995 , Cor. 2 and 3 ) .

###### Remark 3.6 .

### 3.3 Proof

We now continue with the proof of Theorem 3.1. In the following, $L^{p}$ spaces will be on $(M,g)$ with respect to the underlying volume measure. Recall the definition of the bounded class of $W^{1,p}$ functions:

$$ $W^{1,p}(C)=\left\{u\in W^{1,p}(M)\mid\|u\|_{L^{p}},\|\nabla u\|_{L^{p}}\leq C \right\}.$ (19) $$

Step 1. Defining the base function class. Fix a radius $0<r<\operatorname{inj}(M)$, which will be chosen appropriately later. Consider a maximal packing of geodesic $r$-balls, say with centers $p_{1},....,p_{N}$, where $N=N_{r}=N_{r}^{\text{pack}}(M)$ is the packing number. By definition, $B_{r}(p_{i})$ are disjoint for $i=1,...,N$. From Proposition 3.4 the packing number satisfies

$$ $\frac{\mathrm{vol}_{M}}{\mathrm{vol}_{M^{d}_{K}}(B_{2r}(p_{K}))}\leq N_{r}^{ \text{pack}}\leq\frac{\mathrm{vol}_{M^{d}_{K}}(B_{D}(p_{K}))}{\mathrm{vol}_{M^ {d}_{K}}(B_{r}(p_{K}))}.$ (20) $$

For each ball $B_{r}(p_{i})$, we can construct a $\mathcal{C}^{\infty}$ function $\phi^{\prime}_{i}:M\rightarrow[0,r/4]$ with support $\operatorname{supp}(\phi^{\prime}_{i})\subset B_{r}(p_{i})$ such that

$$ $\phi^{\prime}_{i}(p)=\begin{cases}r/4&d(p,p_{i})\leq r/2\\ 0&d(p,p_{i})\geq r\end{cases}.$ (21) $$

This can be done by constructing a cutoff function and finding an appropriate smooth approximation for separable Riemannian manifolds, using infimal convolutions and $\mathcal{C}^{\infty}$ partitions of unity (Azagra et al., , Cor. 3). In particular, we can choose $\phi^{\prime}_{i}$ to have $|\nabla\phi^{\prime}_{i}|\leq 1$ pointwise. From 21, we have the $L^{1}$ lower bound

$$ $\|\phi^{\prime}_{i}\|_{L^{1}(\mathrm{vol}_{M})}\geq(r/4)\mathrm{vol}_{M}(B_{r/ 2}(p_{i})),$ (22) $$

Moreover, we have the $L^{p}$ bounds on $\phi^{\prime}_{i}$ and $\nabla\phi^{\prime}_{i}$

$$ $\displaystyle\|\phi^{\prime}_{i}\|_{L^{p}(\mathrm{vol}_{M})}\leq(r/4)\mathrm{ vol}_{M}(B_{r}(p_{i}))^{1/p},$ (23) $\displaystyle\|\nabla\phi^{\prime}_{i}\|_{L^{p}(\mathrm{vol}_{M})}\leq\mathrm{ vol}_{M}(B_{r}(p_{i}))^{1/p}.$ (24) $$

Therefore, for $r<4$, we have that $\phi^{\prime}_{i}\in W^{1,p}(\mathrm{vol}_{M}(B_{r}(p_{i}))^{1/p})$. Defining

$$ $\phi_{i}\coloneqq\frac{\phi_{i}^{\prime}}{\mathrm{vol}_{M}(B_{r}(p_{i}))^{1/p}},$ (25) $$

we get a non-negative function $\phi_{i}$ with support in $B_{r}(p_{i})$ satisfying:

$$ $\|\phi_{i}\|_{L^{1}(\mathrm{vol}_{M})}\geq(r/4)\frac{\mathrm{vol}_{M}(B_{r/2}( p_{i}))}{\mathrm{vol}_{M}(B_{r}(p_{i}))^{1/p}},\quad\phi_{i}\in W^{1,p}(1).$ (26) $$

Moreover, $\phi_{i}=r/(4\mathrm{vol}_{M}(B_{r}(p_{i}))^{1/p})$ on $B_{r/2}(p_{i})$. We now consider the function class

$$ $F_{r}=\left\{f_{a}=\frac{1}{N_{r}^{1/p}}\sum_{i=1}^{N_{r}}a_{i}\phi_{i}\, \middle|\,a_{i}\in\{\pm 1\},\,i=1,...,N_{r}\right\}.$ (27) $$

Since the sum is over functions of disjoint support, we have that $\|f_{a}\|_{p},\|\nabla f_{a}\|_{p}\leq 1$, and thus each element of $F_{r}$ also lies in $W^{1,p}(1)$. Moreover, every element $f_{a}\in F_{r}$ satisfies the $L^{1}$ lower bound using 26:

$$ $\|f_{a}\|_{L^{1}(\mathrm{vol}_{M})}\geq\frac{r}{4N_{r}^{1/p}}\sum_{i=1}^{N_{r} }\frac{\mathrm{vol}_{M}(B_{r/2}(p_{i}))}{\mathrm{vol}_{M}(B_{r}(p_{i}))^{1/p}} ,\quad\forall f_{a}\in F_{r}.$ (28) $$

Step 2. $L^{1}$-well-separation of $F_{r}$.
Consider the following lemma, which shows existence of a large well-separated subset of $\ell_{1}^{m}$.

###### Lemma 3.7 ( Lorentz et al. 1996 , Lem. 2.2 ) .

There exists a set $G\subset\{\pm 1\}^{m}$ of cardinality at least $2^{m/16}$ such that for any $v\neq v^{\prime}\in G$, the distance $\|v-v^{\prime}\|_{\ell_{1}^{m}}\geq m/2$. In particular, any two elements differ by at least $m/4$ entries.

In particular, let $G\subset\{\pm 1\}^{N_{r}}$ be well separated by the above lemma. Denote by $F_{r}(G)$ the subset of $F_{r}$ corresponding to these indices:

$$ $F_{r}(G)=\{f_{a}\in F_{r}\mid a\in G\}.$ (29) $$

For the specific choice of separated $G\subset\{\pm 1\}^{N_{r}}$ in the above lemma, we claim the following well-separation.

###### Claim 1 .

There exists constant $c>0$ such that for any $f\neq f^{\prime}\in F_{r}(G)$, we have

$$ $\|f-f^{\prime}\|_{L^{1}(\mathrm{vol}_{M})}\geq c>0.$ (30) $$

###### Proof.

Suppose $f\neq f^{\prime}\in F_{r}(G)$. In particular, they are generated by multi-indices $a\neq a^{\prime}\in G$. Consider the set of indices $\mathcal{I}\subset[N_{r}]$ such that $a_{i}\neq a^{\prime}_{i}$. By construction in Lemma 3.7, $|\mathcal{I}|\geq N_{r}/4$. Then the difference between $f$ and $f^{\prime}$ on $B_{r}(p_{i})$ is $2\phi_{i}/N_{r}^{1/p}$ if $i\in\mathcal{I}$, and 0 otherwise. By disjointness of the $B_{r}(p_{i})$, we have

$$ $\displaystyle\|f-f^{\prime}\|_{L^{1}(\mathrm{vol}_{M})}$ $\displaystyle=\sum_{i\in\mathcal{I}}\frac{2}{N_{r}^{1/p}}\|\phi_{i}\|_{L^{1}} \geq\frac{r}{2N_{r}^{1/p}}\sum_{i\in\mathcal{I}}\frac{\mathrm{vol}_{M}(B_{r/2} (p_{i}))}{\mathrm{vol}_{M}(B_{r}(p_{i}))^{1/p}}$ (31) $\displaystyle\geq\sum_{i\in\mathcal{I}}\frac{r\int_{0}^{r/2}s^{d-1}\mathrm{vol }_{M}(B_{r}(p_{i}))}{2N_{r}^{1/p}\int_{0}^{r}s^{d-1}\mathrm{vol}_{M}(B_{r}(p_{ i}))^{1/p}}\qquad\text{where }s(u)=\sinh(u\sqrt{|K|})$ (32) $\displaystyle\geq\frac{rN_{r}^{1-1/p}\int_{0}^{r/2}s^{d-1}}{8\int_{0}^{r}s^{d- 1}}\inf_{i\in\mathcal{I}}\left[\mathrm{vol}_{M}(B_{r}(p_{i}))^{1-1/p}\right]$ (33) $\displaystyle\geq\frac{rN_{r}^{1-1/p}\int_{0}^{r/2}s^{d-1}}{8\int_{0}^{r}s^{d- 1}}\inf_{i\in[N_{r}]}\left[\mathrm{vol}_{M}(B_{r}(p_{i}))^{1-1/p}\right]$ (34) $$

by the $L^{1}$-bound on $\phi_{i}$, Bishop-Gromov (Corollary 2.8), and using $|I|\geq N_{r}/4$ for the inequalities respectively.
∎

This shows $L^{1}$-well separation of the subset $F_{r}(G)\subset F_{r}\subset W^{1,p}(1)$, which consist of sums of disjoint cutoff functions. The key will now be to contrast this with the metric entropy bounds in Lemma 3.5, by showing that $F_{r}(G)$ is difficult to approximate with low pseudo-dimension function classes.

Step 3a. Construction of well-separated bounded set. Let ${\mathcal{H}}^{n}$ be a given set of $\mathrm{vol}_{M}$-measurable functions with $\dim_{p}({\mathcal{H}}^{n})\leq n$. Let $\varepsilon>0$. Denote

$$ $\delta=\sup_{f\in F_{r}(G)}\inf_{h\in{\mathcal{H}}^{n}}\|f-h\|_{L^{1}(\mathrm{ vol}_{M})}+\varepsilon=\operatorname{dist}(F_{r}(G),{\mathcal{H}}^{n},L^{1}( \mathrm{vol}_{M}))+\varepsilon.$ (35) $$

Define a projection operator $P:F_{r}(G)\rightarrow{\mathcal{H}}^{n}$, mapping any $f\in F_{r}(G)$ to any element $Pf$ in ${\mathcal{H}}^{n}$ such that

$$ $\|f-Pf\|_{L^{1}(\mathrm{vol}_{M})}\leq\delta.$ (36) $$

We introduce the clamping operator for a function $f$:

$$ $\displaystyle\beta_{i}=r/(4\mathrm{vol}_{M}(B_{r}(p_{i}))^{1/p}N_{r}^{1/p}), \quad i=1,...,N_{r}$ (37) $\displaystyle({\mathcal{C}}f)(x)=\begin{cases}-\beta_{i},&x\in B_{r}(p_{i}) \text{ and }f(x)<-\beta_{i};\\ f(x),&x\in B_{r}(p_{i})\text{ and }-\beta_{i}\leq f(x)\leq\beta_{i};\\ \beta_{i},&x\in B_{r}(p_{i})\text{ and }f(x)>\beta_{i};\\ 0,&\text{otherwise}.\\ \end{cases}$ (38) $$

Note that $\beta_{i}$ are the bounds of $f_{a}\in F_{r}$ in the balls $B_{r}(p_{i})$. Now consider the set of functions ${\mathcal{S}}\coloneqq{\mathcal{C}}PF_{r}(G)$. Suppose $f\neq f^{\prime}\in F_{r}(G)$. We show separation in ${\mathcal{S}}$:

$$ $\displaystyle\|{\mathcal{C}}Pf-{\mathcal{C}}Pf^{\prime}\|_{L^{1}(\mathrm{vol}_ {M})}\geq\|f-f^{\prime}\|_{L^{1}}-\|f-{\mathcal{C}}Pf\|_{L^{1}}-\|f^{\prime}-{ \mathcal{C}}Pf^{\prime}\|.$ (39) $$

For any $a\in G$, we have that $f_{a}\leq\beta_{i}$ in $B_{r}(p_{i})$, and both $f_{a}$ and ${\mathcal{C}}Pf_{a}$ are zero on $M\setminus\bigsqcup_{i}B_{r}(p_{i})$. We thus have that for any $x\in M$ and any $f_{a}\in F_{r}(G)$,

$$ $|f_{a}(x)-{\mathcal{C}}Pf_{a}(x)|\leq|f_{a}(x)-Pf_{a}(x)|.$ (40) $$

This inequality holds for $x\in B_{r}(p_{i})$ since $\mathcal{C}$ clamps $Pf_{a}(x)$ towards $[-\beta_{i},\beta_{i}]$, and holds trivially $M\setminus\bigsqcup_{i}B_{r}(p_{i})$. Integrating and using 36, we have that for any $f_{a}\in F_{r}(G)$,

$$ $\|f_{a}-{\mathcal{C}}Pf_{a}\|_{L^{1}}\leq\|f_{a}-Pf_{a}\|_{L^{1}}\leq\delta.$ (41) $$

Using 39, 41 and 34, we thus have separation

$$ $\displaystyle\|{\mathcal{C}}Pf-{\mathcal{C}}Pf^{\prime}\|_{L^{1}(\mathrm{vol}_ {M})}$ $\displaystyle\geq\|f-f^{\prime}\|_{L^{1}}-2\delta$ (42) $\displaystyle\geq\frac{rN_{r}^{1-1/p}\int_{0}^{r/2}s^{d-1}}{8\int_{0}^{r}s^{d- 1}}\inf_{i\in[N_{r}]}\left[\mathrm{vol}_{M}(B_{r}(p_{i}))^{1-1/p}\right]-2\delta.$ (43) $$

For ease of notation, define the $L^{1}$-separation distance to be $C_{1}(r)$:

$$ $C_{1}(r)=\frac{rN_{r}^{1-1/p}\int_{0}^{r/2}s^{d-1}}{8\int_{0}^{r}s^{d-1}}\inf_ {i\in[N_{r}]}\left[\mathrm{vol}_{M}(B_{r}(p_{i}))^{1-1/p}\right].$ (44) $$

Step 3b. Minimum distance by contradiction. Suppose for contradiction that $\delta\leq C_{1}(r)/4$. Then from 42, we have

$$ $\|{\mathcal{C}}Pf-{\mathcal{C}}Pf^{\prime}\|_{L^{1}(\mathrm{vol}_{M})}\geq C_{ 1}(r)/2.$ (45) $$

In particular, the separation implies that the ${\mathcal{C}}Pf$ are distinct for distinct $f\in F_{r}(G)$, thus $|\mathcal{S}|=|G|\geq 2^{N_{r}/16}$.

Define $\alpha=C_{1}(r)/2$. Consider the metric entropy in $L^{1}$, as given in Lemma 3.5. By construction 42, $\mathcal{S}$ itself is an $\alpha$-separated subset in $L^{1}$ as any two elements are $L^{1}$-separated by $\alpha$, so

$$ ${\mathcal{M}}_{\alpha}(\mathcal{S},L^{1}(\mathrm{vol}_{M}))\geq 2^{N_{r}/16}.$ (46) $$

We now wish to obtain an upper bound on ${\mathcal{M}}_{\alpha}({\mathcal{S}},L^{1})$ using Lemma 3.5. From the definition of pseudo-dimension, we have $\dim_{p}({\mathcal{C}}PF_{r}(G))\leq\dim_{p}(PF_{r}(G))$, since any P-shattering set for ${\mathcal{C}}PF_{r}(G)$ will certainly P-shatter $PF_{r}(G)$. Since $PF_{r}(G)\subset{\mathcal{H}}^{n}$, we have $\dim_{p}(PF_{r}(G))\leq\dim_{p}({\mathcal{H}}^{n})\leq n$. Thus $\dim_{p}(\mathcal{S})=\dim_{p}({\mathcal{C}}PF_{r}(G))\leq n$. ${\mathcal{S}}$ is $L^{1}$-separated with distance at least $\alpha$, and moreover consists of elements that are bounded by $\beta\coloneqq\sup_{i}\beta_{i}$. We can now use 18 in Lemma 3.5 to obtain:

$$ $M_{\alpha}(\mathcal{S},L^{1}(\mathrm{vol}_{M}))\leq e(n+1)\left(\frac{4e\beta \mathrm{vol}(M)}{\alpha}\right)^{n}.$ (47) $$

Intuitively, $N_{r}\sim r^{-d}$, so the lower bound 46 is exponential in $r$. Meanwhile, $\alpha$ and $\beta$ are both polynomial in $r$, so the upper bound 47 is polynomial in $r$. So for sufficiently small $r$, we have a contradiction, which gives a lower bound on $\delta$. We now show this formally. Recall:

$$ $\beta=\sup_{i\in[N_{r}]}\frac{r}{4\mathrm{vol}_{M}(B_{r}(p_{i}))^{1/p}N_{r}^{1 /p}},\,\alpha=\frac{rN_{r}^{1-1/p}\int_{0}^{r/2}s^{d-1}}{16\int_{0}^{r}s^{d-1} }\inf_{i\in[N_{r}]}\left[\mathrm{vol}_{M}(B_{r}(p_{i}))^{1-1/p}\right].$ (48) $$

Note that the supremum in $\beta$ and the infimum in $\alpha$ is attained by the same $i\in[N_{r}]$, namely, the $p_{i}$ that has smallest $\mathrm{vol}_{M}(B_{r}(p_{i}))$.(^4^44Intuitively, tall thin functions have the worst $L^{1}$ to $L^{p}$ ratio compared to short fat functions. By construction, small balls have tall thin functions.) Combining 46 and 47, where $s(u)=\sinh(u\sqrt{|K|})$,

$$ $\displaystyle 2^{N_{r}/16}$ $\displaystyle\leq e(n+1)\left(\frac{4e\beta\mathrm{vol}(M)}{\alpha}\right)^{n}$ $\displaystyle=e(n+1)\left(\frac{e\mathrm{vol}(M)\sup_{i\in[N_{r}]}[r/(4\mathrm {vol}_{M}(B_{r}(p_{i}))^{1/p})]}{\frac{rN_{r}^{1-1/p}\int_{0}^{r/2}s^{d-1}}{16 \int_{0}^{r}s^{d-1}}\inf_{i\in[N_{r}]}\left[\mathrm{vol}_{M}(B_{r}(p_{i}))^{1- 1/p}\right]}\right)^{n}$ $\displaystyle=e(n+1)\left(16e\frac{\mathrm{vol}(M)\int_{0}^{r}s^{d-1}}{N_{r} \int_{0}^{r/2}s^{d-1}}\sup_{i,j\in[N_{r}]}\frac{\mathrm{vol}_{M}(B_{r}(p_{j})) ^{1/p-1}}{\mathrm{vol}_{M}(B_{r}(p_{i}))^{1/p}}\right)^{n}$ $\displaystyle=e(n+1)\left(16e\frac{\mathrm{vol}(M)\int_{0}^{r}s^{d-1}}{N_{r} \int_{0}^{r/2}s^{d-1}}\sup_{i\in[N_{r}]}\left[\mathrm{vol}_{M}(B_{r}(p_{i}))^{ -1}\right]\right)^{n}$ (49) $$

where the equalities come from definition of $\beta$ and $\alpha$ and rearranging, and the last equality from noting the supremum is attained when $i=j\in[N_{r}]$ minimizes $\mathrm{vol}_{M}(B_{r}(p_{i}))$. Recall from 20 that

$$ $N_{r}\geq\frac{\mathrm{vol}(M)}{\mathrm{vol}_{M^{d}_{K}}(B_{2r}(p_{K}))}.$ (50) $$

###### Proposition 3.8 ( Croke 1980 , Prop. 14 ) .

For $r\leq\operatorname{inj}(M)/2$, the volume of the ball satisfies

$$ $\mathrm{vol}_{M}(B_{r}(p))\geq C_{2}(d)r^{d},\quad C_{2}(d)=\frac{2^{d-1} \mathrm{vol}_{M_{1}^{d-1}}(B_{1})^{d}}{d^{d}\mathrm{vol}_{M_{1}^{d}}(B_{1})^{d -1}}.$ (51) $$

We note that a closed-form for the volume of a $d$-dimensional ball is given as

$$ $\mathrm{vol}_{M_{1}^{d}}(B_{1})=\frac{2\pi^{d/2}}{\Gamma(d/2)},$ (52) $$

where $\Gamma$ is Euler’s gamma function satisfying $\Gamma(n)=(n-1)!$ if $n$ is a positive integer and $\Gamma(n+1/2)=(n-1/2)(n-3/2)...(1/2)\pi^{1/2}$ if $n$ is a non-negative integer. Moreover, the volume of the $d$-dimensional hyperbolic sphere with sectional curvature $K$ is

$$ $\mathrm{vol}_{M_{K}^{d}}(B_{\rho})=\mathrm{vol}_{M_{1}^{d}}(B_{1})\int_{t=0}^{ \rho}\left(\frac{\sinh(\sqrt{|K|}t)}{\sqrt{|K|}}\right)^{d-1}\mathop{}\! \mathrm{d}t$ (53) $$

Note that $\sinh(x)\leq 2x$ for $x\in[0,2]$. Therefore, for $\rho\leq 2/\sqrt{|K|}$, we have $\sinh(\sqrt{|K|}t)\leq 2\sqrt{|K|}t$. We thus have that

$$ $\begin{split}\mathrm{vol}_{M_{K}^{d}}(B_{\rho})&=\mathrm{vol}_{M_{1}^{d}}(B_{1 })\int_{t=0}^{\rho}\left(\frac{\sinh(\sqrt{|K|}t)}{\sqrt{|K|}}\right)^{d-1} \mathop{}\!\mathrm{d}t\\ &<\mathrm{vol}_{M_{1}^{d}}(B_{1})2^{d-1}\rho^{d}/d=C_{3}(d)\rho^{d},\quad\rho \leq 2/\sqrt{K},\end{split}$ (54) $$

where

$$ $C_{3}(d)=\mathrm{vol}_{M_{1}^{d}}(B_{1})2^{d-1}/d.$ (55) $$

We continue the inequality 49 for $r<1/\sqrt{|K|}$:

$$ $\displaystyle 2^{N_{r}/16}$ $\displaystyle\leq e(n+1)\left(16e\frac{{\color[rgb]{0,0,1}\definecolor[named]{ pgfstrokecolor}{rgb}{0,0,1}\mathrm{vol}(M)}\int_{0}^{r}s^{d-1}}{{\color[rgb]{ 0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}N_{r}}\int_{0}^{r/2}s^{d- 1}}{\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}\sup_{i \in[N_{r}]}\left[\mathrm{vol}_{M}(B_{r}(p_{i}))^{-1}\right]}\right)^{n}$ (56) $\displaystyle\leq e(n+1)\left(16e{\color[rgb]{0,0,1}\definecolor[named]{ pgfstrokecolor}{rgb}{0,0,1}\mathrm{vol}_{M^{d}_{K}}(B_{2r})}\frac{\int_{0}^{r} s^{d-1}}{\int_{0}^{r/2}s^{d-1}}{\color[rgb]{1,0,0}\definecolor[named]{ pgfstrokecolor}{rgb}{1,0,0}C_{2}^{-1}r^{-d}}\right)^{n}$ using 20 , Prop. 3.8 (57) $\displaystyle\leq e(n+1)\left(16eC_{3}(2r)^{d}\frac{\int_{0}^{r}s^{d-1}}{\int_ {0}^{r/2}s^{d-1}}C_{2}^{-1}r^{-d}\right)^{n}$ using 54 (58) $\displaystyle\leq e(n+1)\left(2^{d+4}eC_{3}\frac{\int_{0}^{r}s^{d-1}}{\int_{0} ^{r/2}s^{d-1}}C_{2}^{-1}\right)^{n}$ (59) $$

We note that

$$ $\frac{\int_{0}^{r}s^{d-1}}{\int_{0}^{r/2}s^{d-1}}=\frac{\int_{0}^{r}\sinh( \sqrt{|K|}u)^{d-1}\mathop{}\!\mathrm{d}u}{\int_{0}^{r/2}\sinh(\sqrt{|K|}u)^{d- 1}\mathop{}\!\mathrm{d}u}\leq 2^{d}\quad\text{ for $r<1/\sqrt{K}$,}$ (60) $$

since $u\leq\sinh u\leq 2u$ for $u\in[0,2]$. We thus have for $r\leq 1/\sqrt{|K|}$, that

$$ $2^{N_{r}/16}\leq e(n+1)C_{4}(d)^{n},$ (61) $$

where

$$ $\begin{split}C_{4}&=C_{4}(d)=2^{2d+4}eC_{3}C_{2}^{-1}\\ &=2^{2d+4}ed^{d-1}\left[\frac{\mathrm{vol}_{M_{1}^{d}}(B_{1})}{\mathrm{vol}_{M _{1}^{d-1}}(B_{1})}\right]^{d}\end{split}$ (62) $$

We get a contradiction if

$$ $N_{r}>16\left[n\log_{2}C_{4}+\log_{2}\left(e(n+1)\right)\right]$ (63) $$

Recalling the lower bound 20 on $N_{r}$ and using 60,

$$ $\displaystyle N_{r}$ $\displaystyle\geq\frac{\mathrm{vol}(M)}{\mathrm{vol}_{M^{d}_{K}}(B_{2r})}> \frac{\mathrm{vol}(M)}{C_{3}(2r)^{d}}.$ (64) $$

Take the following choice of $r$:

$$ $r=\min\left(\frac{1}{2}\left(16\frac{C_{3}}{\mathrm{vol}(M)}\left[n\log_{2}C_{ 4}+\log_{2}(e(n+1))\right]\right)^{-1/d},\frac{1}{\sqrt{|K|}},\frac{ \operatorname{inj}(M)}{2},4\right)$ (65) $$

Using 64, this choice of $r$ satisfies the contradiction condition 63. Note $r\sim n^{-1/d}$. The constants $C_{3},C_{4}$ depend only on $d$.

Step 4. Concluding contradiction. This choice of $r$ contradicts the assumption that $\delta\leq C_{1}(r)/4$. Therefore, we must have that $\delta>C_{1}/4$. Since the choice of $r$ is independent of the choice of $\varepsilon>0$ taken at the start of Step 3a, we have that

$$ $\operatorname{dist}(F_{r}(G),{\mathcal{H}}^{n},L^{1}(\mathrm{vol}_{M}))\geq C_ {1}(r)/4,$ (66) $$

where $r$ is chosen as in 65. We obtain the chain of inequalities

$$ $\displaystyle\operatorname{dist}(W^{1,p}(1),{\mathcal{H}}^{n},L^{q})$ $\displaystyle\geq\operatorname{dist}(W^{1,p}(1),{\mathcal{H}}^{n},L^{1}) \mathrm{vol}(M)^{1/q-1}$ $\displaystyle\geq\operatorname{dist}(F_{r}(G),{\mathcal{H}}^{n},L^{1})\mathrm{ vol}(M)^{1/q-1}$ $\displaystyle\geq C_{1}(r)\mathrm{vol}(M)^{1/q-1}/4$ $\displaystyle=\frac{rN_{r}^{1-1/p}\int_{0}^{r/2}s^{d-1}}{32\int_{0}^{r}s^{d-1} }\inf_{i\in[N_{r}]}\left[\mathrm{vol}_{M}(B_{r}(p_{i}))^{1-1/p}\right]\mathrm{ vol}(M)^{1/q-1},$ (67) $$

where the first inequality comes from Hölder’s inequality $\|u\|_{1}\leq\|u\|_{q}\mathrm{vol}(M)^{1-1/q}$, the second inequality from $F_{r}(G)\subset W^{1,p}(1)$, the third from 66 and the equality from definition 44 of $C_{1}(r)$.

We conclude with recalling the bounds 60, 64, and Proposition 3.8. Together we have

$$ $\displaystyle\operatorname{dist}(W^{1,p}(1),{\mathcal{H}}^{n},L^{q})$ $\displaystyle\geq\frac{r}{32}\underbrace{\left(\frac{\mathrm{vol}(M)}{C_{3}(2r )^{d}}\right)^{1-1/p}}_{\text{\lx@cref{refnum}{eq:NrExplicitLowerBound}}} \underbrace{2^{-d}}_{\text{\lx@cref{refnum}{eq:MdKUpperBd}}}\underbrace{\left[ C_{2}r^{d}\right]^{1-1/p}}_{\text{Prop. \ref{prop:smallBall}}}\mathrm{vol}(M)^ {1/q-1}$ $\displaystyle=C_{5}(d,\mathrm{vol}(M),p,q)r.$ $$

The constant is

$$ $C_{5}=2^{-d-5}\frac{\mathrm{vol}(M)^{1/q-1/p}}{2^{d-d/p}}(C_{2}C_{3}^{-1})^{1- 1/p}$ (68) $$

Moreover, the constant $C_{5}$ and choice of $r$ are independent of ${\mathcal{H}}^{n}$. Taking infimum over all choice of ${\mathcal{H}}^{n}$ with $\dim_{P}({\mathcal{H}}^{n})\leq n$ and using 65, we have

$$ $\rho_{n}(W^{1,p}(1),L^{q})\geq C_{5}(d,\mathrm{vol}(M),p,q)r\gtrsim(n+\log n)^ {-1/d}.$ (69) $$

###### Lemma 3.7 ( Lorentz et al. 1996 , Lem. 2.2 ) .

###### Claim 1 .

###### Proof.

###### Proposition 3.8 ( Croke 1980 , Prop. 14 ) .

## 4 Conclusion

This work provides a theoretical motivation to further explore the manifold hypothesis. We show that the problem of approximating a bounded class of Sobolev functions depends only on the intrinsic properties of the space it is supported in. More precisely, the approximation error of the bounded $W^{1,p}$ space with respect to bounded pseudo-dimension classes is shown to be at least $(n+\log n)^{-1/d}$, where $d$ is the intrinsic dimension of the underlying manifold. Since generalization error is linear in pseudo-dimension, this provides a latent-dimension-free lower-bound on generalization error. This is in contrast to many works in the literature that provide constructive upper bounds on generalization error based on ReLU approximation properties that still depend on the embedding of the manifold in latent Euclidean space.

The proposed bound can be improved in multiple ways. Firstly, the analysis is restricted to one weak derivative. The analogous result of approximating $W^{k,p}$ in the cube $[0,1]^{D}$ has lower bounds $\sim n^{-k/D}$ (Maiorov & Ratsaby, ). Extending our analysis to more weak derivatives would require a careful construction in Step 1 of test functions with the appropriate regularity conditions. In particular, we would require cutoff functions $\operatorname{supp}\phi_{i}\subset B_{r}(p_{i})$ with explicitly bounded higher weak derivatives $\|\nabla^{\alpha}\phi_{i}\|\lesssim r^{|\alpha|}$, which do not seem to appear in the literature. The explicit construction of Moulis () of a $\mathcal{C}^{\infty}$ function that approximates a $\mathcal{C}^{2k-1}$ function in the $\mathcal{C}^{k}$-topology could be useful in this. Moreover, the current bound requires knowledge of the injectivity radius to uniformly lower-bound the volume of small balls. Other ways of constructing volume lower-bounds would help in improving the constants in the bound.

## Appendix A Sample complexity

For completeness, we briefly formalize the sample complexity bound Proposition 2.3, based on (Anthony & Bartlett, ).

###### Definition A.1 ( Anthony & Bartlett 1999 , Def. 16.4 ) .

For a set of functions $F$, an *approximate sample error minimizing (approximate-SEM)* algorithm ${\mathcal{A}}(F,z)$ takes any finite number of samples $z=(x_{i},y_{i})_{i=1}^{m}$ in $\cup_{m=1}^{\infty}(X\times\mathbb{R})^{m}$ and an error bound $\epsilon>0$, and outputs an element $f\in F$ satisfying

$$ ${\mathcal{R}}({\mathcal{A}}(F,z))<\inf_{f\in F}{\mathcal{R}}(f)+\epsilon,\quad {\mathcal{R}}_{m}(f)=\sum_{i=1}^{m}(f(x_{i})-y_{i})^{2}.$ (70) $$

###### Definition A.2 ( Anthony & Bartlett 1999 , Def. 16.1 ) .

For a set of functions $F$ mapping from domain $X$ to $[0,1]$, a *learning algorithm* $L$ for $F$ is a function taking any finite number of samples,

$$ $L:\bigcup_{m=1}^{\infty}(X\times\mathbb{R})^{m}\rightarrow F$ (71) $$

with the following property. For any $\epsilon,\delta\in(0,1)$, there is an integer $m_{0}(\epsilon,\delta)$ such that if $m\geq m_{0}(\epsilon,\delta)$, the following holds for any probability distribution $P$ on $X\times[0,1]$.

If $z$ is a training sample of length $m$ according to the product distribution $P^{m}$ (i.i.d. samples), then with probability at least $1-\delta$, the function $L(z)$ output by $L$ is such that

$$ $\mathbb{E}_{(x,y)\sim P}[L(z)(x)-y]^{2}<\inf_{f\in F}\mathbb{E}_{(x,y)\sim P}[ f(x)-y]^{2}+\epsilon.$ (72) $$

In other words, given $m\geq m_{0}$ training samples, the squared-risk of the learning algorithm’s output is $\epsilon$-optimal with probability at least $1-\delta$.

Observe that the approximate-SEM algorithm works on the empirical risk, while the learning algorithm works on the risk. Relating the two thus gives generalization bounds. The formal version of Proposition 2.3, based on (Anthony & Bartlett, ) is now given as follows.

###### Proposition A.3 ( Anthony & Bartlett 1999 , Thm. 19.2 ) .

Let ${\mathcal{H}}$ be a class of functions mapping from a domain $X$ into $[0,1]\subset\mathbb{R}$, and that ${\mathcal{H}}$ has finite pseudo-dimension. Let $\mathcal{A}$ be any approximate-SEM algorithm, and define for samples $z$, $L(z)=\mathcal{A}(z,16/\sqrt{\text{length}(z)})$. Then $L$ is a learning algorithm for ${\mathcal{H}}$, and its sample complexity is bounded as follows:

$$ $m_{L}(\epsilon,\delta)\leq\frac{128}{\epsilon^{2}}\left(2\dim_{P}({\mathcal{H} })\log\left(\frac{34}{\epsilon}\right)+\log\left(\frac{16}{\delta}\right) \right).$ (73) $$

###### Definition A.1 ( Anthony & Bartlett 1999 , Def. 16.4 ) .

###### Definition A.2 ( Anthony & Bartlett 1999 , Def. 16.1 ) .

###### Proposition A.3 ( Anthony & Bartlett 1999 , Thm. 19.2 ) .

## Appendix B Proof of lemmas.

###### Proposition B.1 (Covering number estimates) .

Suppose $(M,g)$ has curvature lower-bounded by $K\in\mathbb{R}$, diameter $D$ and dimension $d$. Let $M^{d}_{K}$ be the $d$-dimensional model space of curvature $c$ (i.e. sphere, Euclidean space, or hyperbolic space). The packing number $N_{\varepsilon}(M)$ satisfies:

$$ $\frac{\mathrm{vol}(M)}{\mathrm{vol}_{M^{d}_{K}}(B_{2\varepsilon}(p_{K}))}\leq N _{\varepsilon}\leq\frac{\mathrm{vol}_{M^{d}_{K}}(B_{D}(p_{K}))}{\mathrm{vol}_{ M^{d}_{K}}(B_{\varepsilon}(p_{K}))}$ (74) $$

###### Proof.

Let $\{p_{1},...,p_{N_{\varepsilon}}\}$ be an $\varepsilon$-packing of $M$.

Lower bound. By maximality, balls of radius $2\varepsilon$ at the $p_{i}$ cover $M$, so we have by summing over volumes and using Bishop-Gromov:

$$ $\mathrm{vol}(M)\leq\sum_{i=1}^{N_{\varepsilon}}\mathrm{vol}_{M}(B_{2 \varepsilon}(p_{i}))\leq N_{\varepsilon}\mathrm{vol}_{M^{d}_{K}}(B_{2 \varepsilon}).$ (75) $$

Upper bound. Apply Theorem 2.7 with $\varepsilon\leq D$. We have $\mathrm{vol}_{M}(B_{\varepsilon}(p))\leq\mathrm{vol}_{M^{d}_{K}}(B_{
\varepsilon}(p_{K}))$. Since the $\varepsilon$-balls are disjoint, we have by finite additivity and Bishop-Gromov:

$$ $\mathrm{vol}_{M}(M)\geq\sum_{i=1}^{N_{\varepsilon}}\mathrm{vol}_{M}(B_{ \varepsilon}(p_{i}))\geq N_{\varepsilon}\mathrm{vol}_{M}(M)\frac{\mathrm{vol}_ {M^{d}_{K}}(B_{\varepsilon}(p_{K}))}{\mathrm{vol}_{M^{d}_{K}}(B_{D}(p_{K}))}.$ (76) $$

∎

###### Proposition B.1 (Covering number estimates) .

###### Proof.

## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 6
presentation: 3
questions: See above.
soundness: 3
strengths: The paper has a natural motivation, and the concluded rate seems matching that of classical Euclidean case. The presentation is lucid, and the proof sketch and extended discussion is well written. Overall this paper is a solid contribution on the topic of manifold learning.
summary: This paper studies the complexity of Sobolev function class on Riemannian manifolds. Specifically, the paper derives lower bound of the approximation error of a Sobolev ball by a smaller class with complexity bounded as pseudo-dimension. By constructing explicitly functions of bounded Sobolev norm that are separated in $L^1$, the paper connects the packing number of the manifold with a hard-to-learn subclass in the Sobolev ball, thus forcing a larger error/width. The main theorem claims a lower bound that only depends on intrinsic quantities of the manifold.
weaknesses: 1. The result is not too surprising on the 1,p-Sobolev class, and considering that higher Sobolev space can even be an RKHS [1], one would expect major improvement on the rate. Also using volume comparison to control the packing number is rather standard, and one might further ask if the same technique is applicable to metric measure spaces or RCD spaces, though I understand this technicality may not be particularly befitting of this venue.


2. Typos: 
Definition 2.7 is defining packing number not covering number, and also metric entropy is not commonly defined this way. This version of metric entropy is exactly the same as packing number, hence (7) is not needed. (The key proposition C.1 is correct.) $CPf_a$ out side the balls on line 375 is not necessarily 0.

[1] De Vito, Ernesto, Nicole Mücke, and Lorenzo Rosasco. "Reproducing kernel Hilbert spaces on manifolds: Sobolev and diffusion spaces." Analysis and Applications 19.03 (2021): 363-396.

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confidence: 3
contribution: 2
initial_rating: 5
presentation: 2
questions: - The Theorem 1 lower bound (12) has a dependence on $p$, unlike in the Euclidean case (Maiorov and Ratsaby, 1999). Is there a plausible reason for this, i.e. is $p$ there for an inherent reason? 

- Why is Theorem 1 restricted to the case $K < 0$? What is different about the positive curvature case?

- line 535: it is mentioned that the cutoff functions with bounds on higher derivatives is difficult to construct, but I am having trouble seeing why this should be so. Can the authors explain further?

- line 758: The authors say "By maximality, balls of radius $2\epsilon$ at the $p_i$ cover $M$" but why is this true? The manifold can potentially be very narrow.

- line 691: What is the notation $\mathcal{A}(z, 16 / \sqrt{\text{length}(z)})$?

- Should the empirical risk in (56) be scaled by the sample size?
soundness: 2
strengths: For a technical paper, the presentation is approachable and is self-contained (modulo some typos and missing definitions). The paper extends the lower bound proved in (Maiorov and Ratsaby, 1999) to manifolds.
summary: The paper concerns a lower bound for a nonlinear width (involving the pseudo-dimension, a generalized version of the VC dimension) of Sobolev classes over smooth manifolds of dimension $d$. The authors claim that while the manifold can be embedded in a higher dimensional space with dimesion $D \gg d$ the width of the Sobolev class has a lower bound that depends only on the dimension $d$ of the manifold.
weaknesses: - Not only is the result of Theorem 1 independent of ambient dimension $D$, the ambient dimension does not appear _anywhere_ in the estimates. This is somewhat odd because the abstract mentions the manifold hypothesis which concerns both $D$ and $d$. In some similar approximation bounds, there is typically some dependence on $D$. The authors should address this.

- In a similar vein, the authors do not present a connection between the sample complexity mentioned in Proposition 2.3 and the main Theorem 1, as far as I can see. The assumed connection is that, due to this property of classes with finite pseudo-dimension $\mathcal{H}_n$, the Sobolev class can also be estimated with the sample complexity given in (2), once the approximating class $\mathcal{H}_n$ is determined, it can be estimated with this sample complexity. This connection should be made somewhere.

- The main structure of the proof is almost identical to (Maiorov and Ratsaby, 1999), except the construction of the $L^1$-separated set of functions, due to the domain being a manifold. There are a few questions about the extended lower bound, which I ask below in the "questions" section.

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confidence: 3
contribution: 2
initial_rating: 5
presentation: 3
questions: I don't understand why the main result, Theorem 3.1, is measuring nonlinear n-width between the Sobolev space W^{1,p} and the Lebesgue space L^q. It's really unclear to me why this implies anything about the difficulty in approximating Sobolev space functions. I'd like to see this clarified.
soundness: 4
strengths: The question of why deep neural networks work well on extremely high dimensional data is an important problem and the manifold hypothesis may be a good way to explain this. Work on this problem is important and valuable in machine learning. The problem of lower bounds on complexity is not studied as often as upper bounds. The results appear to be new and non-trivial.
summary: This paper is focused on the manifold assumption in machine learning. The goal is to further shed light on how the intrinsic dimension of the data manifold enters into notions of complexity in function approximation. In particular, the authors prove lower bounds on the complexity of approximating Sobolev space functions on manifolds, and show that the lower bounds, which are essentially 1/n^(1/d), depend only on the intrinsic dimension d of the manifold, and not the ambient dimension of the space the manifold lies in. The authors use a notion of pseudodimension that is an extension of VC-dimension and measure complexity by the nonlinear n-width.
weaknesses: It's not clear to me how applicable these results are in practice. Even when data satisfies the manifold assumption, the intrinsic dimension d may be quite large. It is not clear how large the authors think d is in practice, and how large a d would make these results applicable vs vacuous. For MNIST, for example, it's often given that d is between 7 and 14, depending on the digit. One can assume d is much larger for more challenging problems, maybe 20-40? In this case, the error bound 1/n^(1/d) is vacuous, unless the number of data points n is astronomically large (e.g., if d=20 we need 10^(20) data points!).

Meta-review: The paper studies the complexity of Sobolev spaces on manifolds. Its main result is a lower bound on the error incurred by approximating Sobolev functions in Lq with subspaces of functions learnable from n samples; the bound scales as roughly n^{-1/d} where d is the dimension of the manifold. Here, the constant depends on d, K (a lower bound on curvature), vol(M), p and q. The proof extends an argument of Maiorov and Ratsaby on functions on [0,1]^d by constructing well separated collections of functions on M — roughly, these are W^{1,p} functions localized to a packing set of geodesic balls. The main modification of the argument is to control the effect of curvature (the main difference vis-a-vis the hypercube). The implication of this result for learning is to provide a lower bound for learning (intrinsic) Sobolev functions which depends only on intrinsic quantities. 

This is a technically solid paper, which provides lower bounds on the sample complexity of learning on Riemannian manifolds. The results here are complementary to existing upper bounds for extrinsic learners (such as ReLU networks) on submanifolds. The paper provides lower bounds which are independent of embedding — depending only on the intrinsic properties of M [of course, the sample complexity of extrinsic learners may depend on embedding]. The main concerns pertained to (A) the significance of the technical innovations in the paper, and (B) the practical implications of this setting and results for learning in moderate d.

Decision: Reject