Metric Space Magnitude for Evaluating the Diversity of Latent Representations

The magnitude of a metric space is a novel invariant that provides a measure of the 'effective size' of a space across multiple scales, while also capturing numerous geometrical properties, such as curvature, density, or entropy. We develop a family of magnitude-based measures of the intrinsic diversity of latent representations, formalising a novel notion of dissimilarity between magnitude functions of finite metric spaces. Our measures are provably stable under perturbations of the data, can be efficiently calculated, and enable a rigorous multi-scale characterisation and comparison of latent representations. We show their utility and superior performance across different domains and tasks, including (i) the automated estimation of diversity, (ii) the detection of mode collapse, and (iii) the evaluation of generative models for text, image, and graph data.

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Peer review

Reviewer WoGi7/10 · confidence 4/52024-07-10

Summary

This paper studies the diversity in representation learning. The authors propose a novel family of diversity measures based on metric space magnitude, a mathematical invariant that captures numerous important multi-scale geometric characteristics of metric spaces. The main contributions are as follows: 1. The authors first introduce magnitude as a general tool for evaluating the diversity of latent representations, 2. The authors formalise a notion of difference between the magnitude of two spaces across multiple scales of similarity.

Strengths

1. The idea of introducing magnitude as a general tool for evaluating the diversity of latent representations is quite attractive and it seems can be generalized. 2. The authors formalise a notion of difference between the magnitude of two spaces across multiple scales of similarity. 3. The authors discuss the theoretical properties a suitable diversity measure should satisfy.

Weaknesses

1. As mentioned in the paper, it is difficult to define the diversity. The paper does not clearly explain how to link their method with diversity. Maybe I did't get their point. But I think their method is more like to evaluate the better representation instead of measuring the diversity? 2. The authors demonstrate that magnitude is stable and can detect curvature, however such connection makes people a little confused.

Questions

See weakness. 1. Can authors better explain my confuses? I think the proposed approach is more like to evaluate the better representation instead of measuring the diversity. 2. I didn't see clearly connections between magnitude and curvature. Can authors explain clearly?

Rating

7

Confidence

4

Soundness

3

Presentation

3

Contribution

3

Limitations

See weaknesses.

Authorsrebuttal2024-08-13

Response 1/3

We thank the AC and the reviewers for their further questions and are happy about the chance to elaborate more on the technical details regarding the link between the mathematical theory of diversity and our proposed method. Our previous response aimed to convey an intuitive understanding of diversity. > What are the formal definitions of the four axioms (Monotonicity, Twin property, Absence invariant, Multi-scale)? The first three are somewhat trivial, but how to define the multi-scale i.e., "encodes both local and global trends in the data manifold." mathematically does not seem trivial. > Does the proposed metric MAGAREA satisfy the four axioms? If so, where in the paper can we find the proof? Yes, MAGAREA fulfils all the desired axioms as we will detail below. Definitions of the diversity axioms are stated in Appendix C.3. and we are happy to elaborate on this discussion for revisions. First, we want to highlight what it means for a measure to be multi-scale. A **multi-scale measure** encodes both local and global trends in the data manifold by considering multiple levels of scale or resolution simultaneously. Formally, we can require that a diversity measure $m_t \in \mathbb{R}$ is a continuous function of the scale of dissimilarity $t$. A multi-scale measure, $m$, then summarises diversity across multiple scales i.e. $m = f(m_{t_1}, (m_{t_2}, …, (m_{t_n})) \in \mathbb{R}$ for $n>2$ and some summary function $f$. That is, rather than giving a snapshot of diversity at a fixed degree of (dis)similarity, multi-scale methods summarise diversity across varying scales of (dis)similarity. We reason that this property is advantageous to capture a more complete picture on how both coarse and more nuanced dissimilarities in observations affect diversity. Indeed, **being a multi-scale summary is a distinguishing characteristic of our proposed diversity measure, MAGAREA.** Alternative diversity measures, such as average similarity, the Vendi score or magnitude computed at one scale, do not fulfil this criterion as they are single resolution snapshots computed from a fixed similarity matrix. **For revisions, we will clarify the current statement in the main text and include a formal definition as well as an extended discussion regarding this property in the appendix.** Regarding the remaining properties, we can prove that monotonicity in observations, the twin property and absence invariance hold for the magnitude of a negative definite metric space. Then, because magnitude $Mag_X(t)$ fulfils these axioms for each scale of $t \in \mathbb{R}^+$, our measure $\text{MAGAREA} = \int_{T} Mag_X(t) dt$ does as well. We state this in Appendix C.3. and **will elaborate on the relevant proofs during revisions.** Briefly, we can sketch the relevant proofs as follows: - Magnitude is **monotone in observations** i.e. magnitude does not decrease when including a novel observation. This follows from [Corollary 2.4. in Leinster (2013)](https://arxiv.org/pdf/1012.5857). - Magnitude fulfils the **twin property** i.e. it remains unchanged when including a duplicate observation. This follows from the fact that a metric space cannot contain a duplicate element by definition. - A diversity measure is **absence-invariant** if it remains unchanged when restricting the empirical distribution to its support. That is, diversity does not change when removing elements or features that have not been observed or have zero probability. Magnitude is absence-invariant because the distance matrix $d$ itself is absence-invariant. **We will add this proof during revisions.** We thank the AC for their questions and will highlight the relevant diversity axioms, their definitions and proofs better in revisions. Please let us know whether additional clarifications are required.

Authorsrebuttal2024-08-13

Response 2/3

> Is the design of MAGAREA unique? Cannot we replace $\exp$ in the definition of $\zeta$ with another increasing and convex function? The computation of MAGAREA is unique insofar as we require $\zeta(t)=f(-td)$ being invertible for all $t \in \mathbb{R}^+$, which, to the best of our understanding, won't necessarily hold for any increasing and convex function $f$. For example, take $f(x)=x^{1000}$, so that $\zeta_{ij}(t) = (t d_{ij})^{1000}$ which is increasing and convex for $td > 0$. Then, for some low values of $t$ close to zero, $\zeta$ will be singular and hence not invertible. We know, however, that any positive definite matrix is invertible so that we can define the magnitude of any such matrix $\zeta$ [(Leinster 2017)](https://arxiv.org/pdf/1606.00095). Given this motivation, $\exp$ is a somewhat “canonical” choice for defining the magnitude of a metric space because it is a prime example of a strictly positive definite kernel, which ensures invertibility of the similarity matrix for any negative definite metric $d$ [(Feragen et al. 2015)](https://ieeexplore.ieee.org/document/7298922). From a category-theoretic perspective, magnitude represents a generalised notion of size for a metric space, which is a special type of monoidal categories. The exponential kernel then has been chosen for defining the magnitude of a metric space because of its multiplicative properties i.e. it is necessary that $f(x+y)=f(x) \cdot f(y)$ [(Leinster 2021, p.212)](https://arxiv.org/pdf/2012.02113) to define a valid notion of size. This essentially forces, $f(x) = c^{-x}$ for some constant $c$. Hence, the choice of $e$ as a basis is arbitrary and any other positive constant could be used, which is equivalent to re-scaling the distances. We also want to note that the current definition of magnitude has revealed useful theoretical and geometric insights, such as proving the connection between magnitude and the curvature of a metric space. Hence, if we choose any $f$ that does not fulfil the multiplicative property above, we lose the existing knowledge relating magnitude to geometrical properties of the underlying space, for example. The standard formulation of $\zeta$ also has some appealing properties linking it to diversity. We have $\exp(-td_{ii})=\exp(0)=1$, so that the similarity of an observation to itself is always $1$. Further, we have $\lim_{t \to \infty} \exp(-t d_{ij}) = 0$, so that the similarity between distant points approaches zero asymptotically. Thus, $\zeta(t)$ gives a valid similarity matrix, whose entries are bounded by $[0,1]$, which is desirable for defining a (dis)similarity-based notion of diversity [(Leinster 2021, p.173)](https://arxiv.org/pdf/2012.02113). **In fact this behaviour is the practical reason why magnitude can be interpreted as the effective number of points.** Hence, we know that other choices of $\zeta$ are possible and we believe it is worth investigating the generalisation of our methods to other choices of kernels in future work. Nevertheless, we find that, for now, $\exp$ provides a useful “default” choice for defining diversity based on the considerations detailed above.

Authorsrebuttal2024-08-13

Response 3/3

> Also, as a relevant question, I would like to ask why we cannot use for authors' motivation (traditional) discrepancy measures in the low-discrepancy sequence area, like the star-discrepancy (e.g., [WS08]. We can find traditional discussions in e.g., [War72])? Thank you for pointing out these references! We agree that discrepancy measures, such as star-discrepancy, give an interesting set of tools for quantifying the difference between a latent space and a uniform distribution on a hypercube. In our context, these methods could be used to measure an important aspect of diversity, namely the evenness or uniformity of an empirical distribution. In our previous response, we explained the importance of evenness for understanding diversity via the comparison between the clustered patterns $X_2$ and the random point pattern $X_1$. Preliminary experiments with discrepancy measures on such data show that (a) measures are often not capable of reproducing the ground-truth ranking in diversity, i.e. $X_1, X_2, X_3, X_4$, or (b) fail to capture the degree of difference in diversity when comparing $X_1$ and $X_2$. **We will incorporate these measures in an extended experimental discussion in our revision.** Moreover, we find that “evenness” does not fully capture all relevant aspects of diversity. Another key aspect is measuring the absolute richness of a dataset. For example, we want to measure that a space with 100 uniformly distributed samples does not decrease in diversity when including additional 10 randomly sampled points. That is, we define diversity not just by the evenness of the empirical distribution, but also by the richness or the number of distinct observations or clusters. Linking back to the axioms of diversity, this behaviour is described by requiring monotonicity in observations as well as the twin property. We can show that both axioms do not hold for the star-discrepancy via counterexamples. For example, when including discrepancy measures into the simulation study conducted for the twin property as detailed in the additional PDF, we see that L2-star discrepancy changes under the inclusion of duplicates. In comparison, our proposed diversity measure evaluates both evenness and richness by summarising the effective number of distinct points across multiple resolutions. Therefore, our method gives a unique view on the diversity of latent spaces that is based on mathematical theory, but not yet addressed by existing diversity or discrepancy measures in ML. Regarding the original motivation of our work, we focused on linking our method to two applications that benefit from improved diversity evaluation, namely the evaluation of generative models and automated embedding-based diversity evaluation e.g. for assessing LLMs. To the best of our knowledge, discrepancy measures have not yet been included as standard benchmark methods for diversity evaluation in these fields. **We fully agree that this is worth further investigation and are excited to include discrepancy measures as alternative baselines during revisions.** We thank the AC for their questions as well as their further suggestions and are happy to clarify our answers.

Area Chair cagw2024-08-14

Thank you for your response!

Dear Authors, thank you for your response. We will consider your response in the discussion between Reviewer and AC.

Reviewer cXLQ6/10 · confidence 4/52024-07-10

Summary

This paper focuses on evaluating the diversity of latent representations. The authors develop a family of magnitude-based measures of the intrinsic diversity of latent representations, formalizing a novel notion of dissimilarity between magnitude functions of finite metric spaces. Moreover, they demonstrate the practicality and performance of the proposed measures in different domains and tasks.

Strengths

1. The method proposed in this paper is innovative, and the writing is logical. 2. This paper conducts an in-depth theoretical analysis and sufficient experimental discussion and analysis.

Weaknesses

1. In Section 4.3, the reason for choosing the 5-NN classifier needs to be explained. In addition, what is the purpose of designing a comparative experiment between PCA pre-processing and no pre-processing? 2. This paper discusses several application scenarios of the proposed diversity evaluation, and we can further explore whether there are more scenarios worth exploring, such as measuring the representation ability of graph contrastive learning models.

Questions

1. In Table 1, what is the reason for the apparent performance difference between MAGAREA with the piecewise linear and MAGAREA using quantile regression? 2. In section 4.3, what is the basis for the author to use the 5-NN classifier to predict the embedding model? 3. In section 4.3, what is the purpose of the author's comparison of the results of PCA pre-processing and no pre-processing? How do the authors view that these two methods have both positive and negative effects on different models? 4. In Section 4.5, the authors discussed magnitude evaluation graph generative models. Can the diversity evaluation proposed in this paper also be used to evaluate the representation learning ability of graph contrastive learning models?

Rating

6

Confidence

4

Soundness

3

Presentation

3

Contribution

3

Limitations

1. MAGDIFF is a reference-free measure of intrinsic diversity, but does not measure fidelity. 2. The paper does not investigate whether embedding-based similarities are outperformed by alternative task- or domain-specific similarities. Instead, the evaluation relies on the utility of embedding models and assumes that latent spaces encode useful/realistic relationships between samples.

Reviewer vep56/10 · confidence 3/52024-07-16

Summary

In this paper, authors introduce magnitude of a metric space to evaluate the diversity of the learned latent representation. Extensive experimental results are proposed, which to some extent illustrate the effectiveness of the proposed criterion.

Strengths

1. The expression of the paper is easy to follow. 2. The solving problem is important and could have good impact in the community. 3. The proposed method has shown good theoretical and empirical performance to some extent, illustrating the effectiveness of the proposed measurement. 4. The experimental results are comprehensive.

Weaknesses

1. The theoretical analysis lacks comparison. 2. More discussion should be conducted over the circumstances when the proposed algorithm performs not as good as the existing algorithms to give more understanding of the pros and cons of the proposed criterion. 3. It is welcomed if a case study can be conducted to illustrate more insight of the proposed criterion.

Questions

1. More theoretical comparison between existing methods will make the results more convincing. Is the proposed method superior over other counterparts theoretically? 2. Can the proposed criterion combine with the existing methods to provide more comprehensive description of the learned representation and come up with better performance. Is it compatible with the existing methods?

Rating

6

Confidence

3

Soundness

3

Presentation

3

Contribution

3

Limitations

See the questions and weakness of the paper.

Authorsrebuttal2024-08-12

Thank you very much for your positive response. We are pleased to hear the additional materials and clarifications have provided a clearer understanding. Should you or any other reviewers have additional questions, we are happy to discuss and provide further clarifications.

Area Chair cagw2024-08-12

Could you answer some further questions?

Dear Authors, Thank you for taking the time to discuss this with the Reviewers. At the request of the Program Chair, I would like to take this opportunity to suggest that you, as Area Chair, respond to the Reviewer's question in more detail, as I believe that the Authors' response was unclear and that this will hinder my writing of a meta-review. The Reviewer originally asked about how the method linked to diversity. I understand that Authors answered the question partially, but I would appreciate it if Authors could answer the following: - What are the formal definitions of the four axioms (Monotonicity, Twin property, Absence invariant, Multi-scale)? The first three are somewhat trivial, but how to define the multi-scale i.e., "encodes both local and global trends in the data manifold." mathematically does not seem trivial. - Does the proposed metric MAGAREA satisfy the four axioms? If so, where in the paper can we find the proof? - Is the design of MAGAREA unique? Cannot we replace $\\exp$ in the definition of $\\zeta$ with another increasing and convex function? Also, as a relevant question, I would like to ask why we cannot use for authors' motivation (traditional) discrepancy measures in the low-discrepancy sequence area, like the star-discrepancy (e.g., [WS08]. We can find traditional discussions in e.g., [War72])? [War72] Warnock, Tony T. "Computational investigations of low-discrepancy point sets." Applications of number theory to numerical analysis. Academic Press, 1972. 319-343. [WS08] Xiaoqun Wang, Ian H. Sloan, Low discrepancy sequences in high dimensions: How well are their projections distributed?, Journal of Computational and Applied Mathematics, Volume 213, Issue 2, 2008, Pages 366-386, Thank you in advance for taking the time for the discussion.

Reviewer vep52024-08-12

Comment

Thanks for the detailed responses, especially the additional discussion, analysis and the example, which solve my concerns and questions.

Program Chairsdecision2024-09-25

Decision

Accept (poster)

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