__ Summary and Contributions__: Update: I'm grateful for the authors' time in responding to the reviewers' questions. However, I was disappointed they did not clarify whether whether any new theoretical results are proved in the paper. Specifically, whether their claim that "the linear peak at N=D is solely due to overfitting the noise in the labels" has a precise, mathematical statement and proof. Addressing this in the paper's introduction will help readers understand the novelty and impact.
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Random feature regression in the high-dimensional limit has been a useful model for double descent behavior, where the test loss decreases, increases, and then decreases again as a function of the number of hidden features for a fixed dataset. The authors discuss the existence of a triple descent phenomenon in the test loss as a function of the dataset size. Specifically, they find a second point of nonmonotonicity when the dataset size and dimensionality are equal. Furthermore, they analyze the test loss using a bias-variance decomposition developed in previous work.

__ Strengths__: The author's discussion of the descent when the dataset size and dimensionality are equal is extensive and has not be highlighted before. They give two interesting perspectives to explain this behavior, namely an algebraic one based on the rank and a bias-variance perspective.
The paper also has many excellent figures that help the presentation and aid in the understanding of their results.

__ Weaknesses__: The paper's primary weakness is that it seems to overload the definition of double descent. Traditionally, the test loss is viewed as a function of the model complexity for a fixed training dataset. This is an advantage of the RF model over previous kernel regression models of double descent, where the model complexity is tied to the training data. It turns out that in this high-dimensional random features setting, only the ratios between the dimensions are relevant, so it is tempting to think that viewing the test loss as a function of width or dataset size is symmetric. However, changing the dataset size, changes its relative size wrt. the data dimension. Fundamentally, this is the reason for the behavior. I'm sure the authors understand this. I just feel it makes the result's relationship with double descent quite different and that it is likely to confuse people.
A second question is whether any new theoretical results, over those of Mei and Montanari, 2019, and d'Ascoli et al., 2020, are required? It seems like the model is the same and the bias-variance decomposition is the same as d'Ascoli. Could the authors clarify this?

__ Correctness__: The results all look correct.

__ Clarity__: I found the paper well written, easy to follow, and an enjoyable read. In particular, the figures are excellent are really benefit the paper. Some of the legends are quite short though. With more room perhaps the authors should consider expanding them.

__ Relation to Prior Work__: The paper is very well referenced and situates itself properly in the field. As I mentioned above, some more details on whether any new theoretical results are required above Mei and Montanari, 2019, and d'Ascoli et al., 2020, would be good.
The paper of Liang et al., "on the multiple descent of minimum-norm interpolants and restricted lower isometry of kernels" is relevant. The authors should also discuss differences with Adlam and Pennington, 2020, which also finds a triple descent phenomenon.

__ Reproducibility__: Yes

__ Additional Feedback__: Line 163: "at" should be "as."
Line 224: There can be training dynamics associated with the RF model. Specifically, it is related to a NN with random first-layer weights, but second-layer weights initialized at 0 and trained via gradient descent.

__ Summary and Contributions__: This paper takes a closer look at the "double descent" and related phenomena, where models "overfit" in some narrow regime of parameters, but behave well outside this regime. This work considers the test risk as a function of: input-dimension (D), model dimension (P), and number of train points (N). It considers stylized models (random-features in theory, and 2-layer networks in practice) which have been studied in previous works, and are reasonable models for studying double-descent.
There are two main contributions:
1. By looking at the landscape as a function of both D and P, they find two distinct kinds of "overfitting" -- one determined by P and one by D. This paper is, as far as I know, the first to point out these two distinct effects.
2. They theoretically analyze these two effects, and show how they depend on problem parameters including the type of non-linearity. (Building on recent works which related the test risk to the spectrum of certain random matrices).
These are significant conceptual contributions which clarify the landscape of overfitting. They only apply to restricted models (random features and 2 layer nets), but it is a promising first step to studying the phenomenon more broadly.

__ Strengths__: The main strengths of this work are its conceptual novelty: pointing out two distinct kinds of overfitting which have been conflated in the past.
An additional strength is the mathematical analysis: the expressions for test risk have existed in past work, but were not studied carefully enough to decouple the two effects presented in this work. It is significant that the mathematical analysis is able to yield insight into the empirical effects.
Broader relevance: This paper continues the line of work studying overparameterized models and their pathologies (double-descent, etc.). This topic has had a resurgence of interest recently, and should be of interest to the NeurIPS community.

__ Weaknesses__: One weakness of this work is the theory and experiments are in toy settings: random features and 2-layer networks on simple ground-truth distributions (linear, or random-relu-teacher).
This is necessary for analytical tractability, but because there are no experiments on real data, it is unclear whether the insights obtained are relevant for neural networks in practice.
It is of course independently interesting to study the dynamics of these toy models, but it would be much more significant if these effects were also demonstrated or investigated on real data.
For example, are the two kinds of overfitting empirically present in say convnets on CIFAR (or even 2-layer nets on MNIST)?
If so, that would strengthen this paper's impact significantly.
If not, why not? -- what separates these more realistic settings from the ones in this paper?

__ Correctness__: There are no major technical issues as far as I am aware.
Some minor issues:
Section 3.2 only tests one fixed value of regularization. If regularization is studied, it makes more sense to optimize over the regularization parameter, and consider only the optimal choice for each setting of (N, D, P).
Section 3.3 says "Since the nonlinear peak is due to vanishingly small eigenvalues, which is not the case of the linear peak,..."
Aren't both peaks due to small eigenvalues in Z^TZ?

__ Clarity__: The clarity of the paper could be improved.
While the gist of the paper is clear, many of the most interesting details are not presented clearly.
Figure 5 is the primary figure which supports the theoretical part of this paper. More effort should be put into making this figure clear. For example, overlaying all of the plots for varying N/D ratios creates visual confusion, and obscures the "gaps" -- which is the crucial part.
I suggest separate plots for each N/D, so that the reader can focus on one setting at a time.
The figures should also be annotated to highlight the interesting features -- the "linear" and "nonlinear" contribution to the spectrum, and the gaps between them.
The text discusses how this gap depends on N/D, although this is unclear from the current figures.
More comments:
- Line 176-177 ("the nonlinear component acts as an implicit regularizationâ€¦") sounds interesting and should be explained more. More generally, the section "Analysis of gaps" should explain more why studying the gap is interestering/important.
- The "3D" plots may be fine once, but I suggest moving to flat 2d scalar-field plots. (3d obscures some of the data and makes comparison across graphs hard, since the z-axis is scaled differently each time).
- The top figure of Figure 4 is not referenced in the text, and it's unclear what this figure is adding.
- Section 2.3 can be removed, or moved to the appendix. The bias-variance has been done in many prior papers by now (including the ones cited). This section does not appear to contain new insights beyond prior work, which already discussed ensembling and regularization. Moreover, the text is too short to be clarifying, and Figure 6 is too dense to parse without discussion (there are 7 overlapping lines per plot, and 4 plots). If there is some message in this section that the authors want to convey, it should be conveyed much more clearly.
- Section 3.1, on the effect of nonlinearity, is interesting and deserves more discussion.

__ Relation to Prior Work__: Prior work needs to be discussed more extensively -- several important citations are missing or incorrect.
The work [A] was one of the first to explicitly study the test loss landscape as a function of model-size and sample-size independently (as opposed to just via their ratio), which is a key aspect of this work.
As far as I am aware, [A] introduced the term "sample-wise non-monotonicity", which is used throughout this paper, but is not cited.
The first citation to [25], on line 75, is incorrect. [25] is not about adversarial training.
There are several references to earlier work missing from Line 36 ("a similar phenomenon has been well-known for several decades for simpler models"): see [C] and [D] from 1990, 1991. These are even earlier than the cited works (from 1996).
Monotonicity of learning algorithms was also studied in [B].
The relation to prior work [12] should be discussed further. It is my understanding that [12] derived the same asymptotics which are studied here, but studied them under a different regime of parameters.
The "triple descent" from [18] is discussed only briefly -- it is mentioned that [18] can be considered as "two linear peaks". This seems worthy of more discussion, especially in the context of this paper: If two linear peaks are possible, then are two non-linear peaks possible as well? In general, this suggests that the picture can be more complex than the "one linear, one nonlinear" story presented in this work. This may be a topic worthy of future work, but some acknowledgement of the complexity of the general setting would clarify even the current work.
[A] Deep Double Descent: Nakkiran, Kaplun, Bansal, Yang, Barak, Sutskever. 2019.
[B] Minimizers of the Empirical Risk and Risk Monotonicity: Loog, Viering, Mey. 2019.
[C] Eigenvalues of covariance matrices: Application to neural-network learning. Le Cun, Kanter, Solla. 1991.
[D] Second Order Properties of Error Surfaces: Learning Time and Generalization. Le Cun, Kanter, Solla. 1990.

__ Reproducibility__: Yes

__ Additional Feedback__:

__ Summary and Contributions__: The paper shows that when you increase the number of samples used for training a model, the expected test error may exhibit a 'triple descent'. The show that this sample-wise triple descent arises in a random features model depending on the non-linearity, which in turn affects the spectrum of the covariates in the regression. They experimentally confirm that this phenomenon arises in a teacher-student setup.

__ Strengths__: This paper adds to the growing body of literature on double descent in machine learning models and shows that there may also sometimes be a triple descent with the number of samples. The paper uses a random features model to analyze this phenomenon and shows that the occurrence of this depends on the non-linearity used in the random features.

__ Weaknesses__: Post-rebuttal: After reading the author responses and reviewer discussion, I think the paper does make a new conceptual contribution by combining different insights even if the technical novelty is not as strong. I have updated my score and look forward to reading future work on more structured datasets.
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Theoretically, the paper does not make a very novel contribution. The main results of the paper rely on the following insights
(a) If the smallest eigenvalue of the co-variance matrix decreases with N, this can cause non-monotonicity in the test error -- this has now been studied in various papers including Advani & Saxe
(b) The non-linearity can be decomposed into a linear and non-linear term - this was shown in previous work as cited by the authors
(c) The spectrum from these two part remain almost independent - this was only shown empirically
Moreover, the evidence that this phenomenon may occur outside of the narrow random feature setting is not very strong. The authors do show that the NN model displays this peak, but even in this case the peak is not clear for ReLU as shown in the supplement. There is also no evidence of this occurring for structured data (There are some experiments in the supplement for MNIST but they do not support the hypothesis as strongly)
Due to a combination of these two factors, it is not very clear to me what the broader takeaways are from this paper (outside of the possible existence of a triple descent)

__ Correctness__: The theoretical claims for the random features model and the subsequent conclusions are correct. The empirical methodology for the NN model is also correct.
Though not central to the main claims, the paper repeatedly mentions that the interpolation threshold is observed when the number of parameters is the same order as the number of samples (eg Line 40-42). The location of the peak can depend on the distribution, architecture and the training algorithm and will not necessarily occur at the order of parameters.

__ Clarity__: There are some areas where the paper could use improvement
1. The plots in the paper are hard to read (Fig 3, 8, 9 and similar figures in the supplement). I would suggest that these be plotted as a 2D figure to improve readability.
2. The results for the NN model are scattered throughout the paper and it would be good to see a summary of what the results are and how much they depend on specific model/data choices.
3. The key takeaways from the paper are not very clear

__ Relation to Prior Work__: The paper has some missing citations to relevant work.
[1] should be cited in the introduction when referring to works that show double descent in deep neural networks and in reference to sample-wise double descent. [2] should also be cited in relation to double descent.
[1] Nakkiran, P., Kaplun, G., Bansal, Y., Yang, T., Barak, B., & Sutskever, I. (2019). Deep double descent: Where bigger models and more data hurt. arXiv preprint arXiv:1912.02292.
Chicago
[2] Nakkiran, P. (2019). More data can hurt for linear regression: Sample-wise double descent. arXiv preprint arXiv:1912.07242.

__ Reproducibility__: Yes

__ Additional Feedback__:

__ Summary and Contributions__: This paper investigates generalization error of random features regression model as a function of sample size / model complexity, and shows that there can be not one but two distinct error peaks: the "nonlinear" peak when sample size equals model complexity N=P; and the "linear" peak when sample size equals input dimensionality N=D. The paper shows that the same can happen in a neural network model.

__ Strengths__: The paper explores the "double descent" phenomenon that has been focus of much work recently. It clearly elucidates a surprising phenomenon that "double descent" can become "triple descent" with two distinct risk peaks. The peaks are shown to have different properties (e.g. only the nonlinear peak survives in the noiseless regime). I think this is an important contribution a clear accept.
Here is the intuitive picture that I formed after reading the paper: imagine a neural network with some hidden layers. It's obvious that when the activation function is linear, the interpolation threshold and the risk peak is at N=D. It's obvious that when the activation function is nonlinear, the interpolation threshold and the risk peak is N=P>D. The paper asks: what happens with the risk peak when the activation function gradually changes from nonlinear to linear? Would the peak slowly move from P to D? The (surprising?) answer is no: the peak at P will go down, the peak at D will go up, and for some time both will co-exist.

__ Weaknesses__: All mathematical development and results here seem to be taken from prior work (so this paper is not math heavy; it's not a weakness but just a remark), but they are well used to explain empirical findings. I do not have any major criticisms but only some more or less minor suggestions to improve the presentation.

__ Correctness__: yes

__ Clarity__: yes

__ Relation to Prior Work__: yes

__ Reproducibility__: Yes

__ Additional Feedback__: This paper investigates generalization error of random features regression model as a function of sample size / model complexity, and shows that there can be not one but two distinct error peaks: the "nonlinear" peak when sample size equals model complexity N=P; and the "linear" peak when sample size equals input dimensionality N=D. The paper shows that the same can happen in a neural network model.
The paper explores the "double descent" phenomenon that has been focus of much work recently. It clearly elucidates a surprising phenomenon that "double descent" can become "triple descent" with two distinct risk peaks. The peaks are shown to have different properties (e.g. only the nonlinear peak survives in the noiseless regime). I think this is an important contribution a clear accept.
Here is the intuitive picture that I formed after reading the paper: imagine a neural network with some hidden layers. It's obvious that when the activation function is linear, the interpolation threshold and the risk peak is at N=D. It's obvious that when the activation function is nonlinear, the interpolation threshold and the risk peak is N=P>D. The paper asks: what happens with the risk peak when the activation function gradually changes from nonlinear to linear? Would the peak slowly move from P to D? The (surprising?) answer is no: the peak at P will go down, the peak at D will go up, and for some time both will co-exist.
All mathematical development and results here seem to be taken from prior work (so this paper is not math heavy; it's not a weakness but just a remark), but they are well used to explain empirical findings. I do not have any major criticisms but only some more or less minor suggestions to improve the presentation.
Major comments
* none [but please respond at least to the last two bullet points in "Medium comments" during the author response period]
Medium comments
* Line 35: the literature review for linear regression can be improved:
a) Refs [13,14,20,21] are not all "several decades" old. I'd suggest to split them into those that are indeed old [13,14] and then write something like "... and has recently been studied in-depth [20,21]".
b) Also, both old/new lists can be extended. For the old: there is Opper 1995 (in The Handbook of Brain Theory and Neural Networks) and there is Duin 1995 http://www.rduin.nl/papers/scia_95.sssize.pdf. There is also Krogh and Hertz 1992 https://iopscience.iop.org/article/10.1088/0305-4470/25/5/020. And Opper 1990: https://iopscience.iop.org/article/10.1088/0305-4470/23/11/012. Maybe cite one paper from each group of authors. Can also cite this recent comment https://www.pnas.org/content/117/20/10625 "A brief prehistory of double descent".
c) For the new: consider adding https://arxiv.org/abs/1805.10939 (in press in JMLR). Disclaimer: I am one of the authors. If you think this paper is irrelevant then feel free not to cite it. Two very recent works in the same vein: https://arxiv.org/abs/2006.05800, https://arxiv.org/abs/2006.06386.
* Figure 2: as drawn, it looks like P<D, but most subsequent figures (e.g. Fig 4 etc) show examples with P>D. Consider modifying Figure 2 to make P>D.
* Figure 2: consider adding another panel for the neural network model described in section 1.2. Line 109 says "with 2 layers" but it's actually not entirely clear if these are 2 hidden layers? or only 1 hidden layer? A figure would make this obvious.
* Wouldn't Figure 3 (and later Figs 8/9) be clearer in 2D instead of 3D? You use color coding for the error anyway. The "curves" would of course become simply straight lines which is probably what you did not want; but the color might be clear enough to show non-monotonic error behaviour along these lines. At least to me, 3D plots are not very easy to comprehend.
* Good point is raised in lines 128-130 about structured data. The papers I mentioned above in the first bullet point, item (c), deal exactly with this situation of structured data (and non-random true beta vector) and show that in linear regression this can bring out a whole range of new phenomenona (e.g. https://arxiv.org/abs/1805.10939).
* Line 162 / Fig 5: why is |x| purely nonlinear (r=0)? This is not very intuitive to me, can you insert some intuition into the text? Are there arny other simple activation functions that are purely nonlinear in the sense that r=0? Would x^2 work (the shape is similar to |x|)? Would x^3 (shape very different but in some sense x^3 is less linear than x^2...)?
* Line 196: would it make sense to have an analogue of Figure 6 (or at least panels (a) and (b)) for linear sigma()? It should illustrate this claim directly. Maybe as a supplementary figure if there is no space in the main text? Or maybe it can be superimposed as thin lines directly into Figure 6? Or are the curves for linear sigma() somehow noninteresting?
* Figure 10 in the suppl materials: the r=0 yellow line is very different from the orange line (r=0.8?), as remarked in the text. Is it really not a smooth change (line 353)? Why don't you add more lines for the values of r between orange and yellow? There is space in the figure. This is related to the word "abruptly" in line 213 of the main text. I think you need to clarify if this means "smooth but fast" or actually "non-smooth phase transition".
* Figure 6 shows that bias term does not show any divergence (following Ref [19]). However, Mei & Montanari claim that the bias term does diverge in random features model: see e.g. page 6, "Bias term also exhibits a singularity at the interpolation threshold" listed as one of the main insights. This is not the primary focus of this work but it seems that Ref [19] and your results shown here are in disagreement with Mei & Montanari (possibly about how to define the bias term correctly?). As you show it here in Figure 6, please comment on this difference to Mei & Montanari.
Minor comments
* Title: consider removing the part after the colon.
* First sentence of the abstract: it only describes "sample-wise" double descent. Maybe formulate such that it refers to both sample-wise and complexity-wise, e.g. "increasing the number of training examples N or the number of parameters P..."
* A recent work on random features model: https://arxiv.org/abs/2006.05013 -- consider citing if relevant (?) The same for https://arxiv.org/abs/2002.08404.
* Bullet list at line 162: mention in the text which activation functions sigma() given each of the r values. It's written in the figure but not in the text.
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POST-REBUTTAL: My score was already 8 so I leave it as it was. There was a very interesting discussion between the reviewers, that I hope the authors will get forwarded by the editor. If so, the authors could work on clarifying some things in the revised version.