Paper:
Eigenvalue Decay Implies Polynomial-Time Learnability for Neural Networks
We consider the problem of learning function classes computed by neural networks with various activations (e.g. ReLU or Sigmoid), a task believed to be computationally intractable in the worst-case. A major open problem is to understand the minimal assumptions under which these classes admit provably efficient algorithms. In this work we show that a natural distributional assumption corresponding to eigenvalue decay of the Gram matrix yields polynomial-time algorithms in the non-realizable setting for expressive classes of networks (e.g. feed-forward networks of ReLUs). We make no assumptions on the structure of the network or the labels. Given sufficiently-strong eigenvalue decay, we obtain fully-polynomial time algorithms in all the relevant parameters with respect to square-loss. This is the first purely distributional assumption that leads to polynomial-time algorithms for networks of ReLUs. Further, unlike prior distributional assumptions (e.g., the marginal distribution is Gaussian), eigenvalue decay has been observed in practice on common data sets.

Review:
This paper considers the problem of square-loss regression for several function classes computed by neural networks. In contrast to what previous hardness results might suggest, they show that those function classes can be improperly learnt under marginal distribution assumptions alone. Specifically, they use previous structural results of approximate RKHS embedding and assume an eigenvalue decay assumption on the empirical gram matrix (namely, it is approximately low-rank). They then apply a recursive Nystrom sampling method to obtain an approximate and compressed version of the gram matrix, which is then used instead in the learning. The error of the resulting regression is then bounded using known compression generalization bounds, hence obtaining
generalization bounds for kernelized regression using Compression Schemes. These in turn are claimed to be tighter than previously used bounds in terms of the required norm regularization (denoted B) in the RKHS. Under the above assumption, this leads to more efficient learning.
Despite the numerous typos and unclear sentences (see below for specific comments), the paper can be well understood for the most part and I found it quite interesting. It non-trivially combines several known results to obtain very nice conclusions about a still not well understood topic. 
The main technical contribution is obtaining generalization bounds for kernelized regression using Compression Schemes. As the authors comment, it is indeed interesting how compression schemes can be used to improve generalization bounds for other function classes. One such work is [KSW], showing that compression-based generalization bounds similar to those considered here can be used to obtain a computationally efficient algorithm for computing a Bayes consistent 1-NN classifier in general metric space.
To be more certain about my review please reply to the following:
- It is claimed that the proposed algorithm has fully-polynomial time complexity in all the relevant parameters. Unfortunately, this dependence is not transparent and is implicit in the results, in particular on its dependence on B. It is only claimed in Thm. 1 that the runtime is O(log B). Please also discuss why a linear dependence in B is considered not fully polynomial.
- The dependence on B is Section 7 is hidden, making it very hard to follow as how the new compression bound comes into play. The proof in the supplementary doesn't help much either. As this is the main advantage, it should be much more transparent.
Specific comments:
- Line 38: "fully polynomial-time algorithms are intractable" is self contradicting
- Line 42: "... are necessary for efficient learnability" - I believe you should add "by gradient based algorithms". Otherwise, the next sentence in the paper is a contradiction.
- A better connection to the results in [Zhang] is needed.
- Thm 1.: the role of \eps should be introduced, even if informally.
- Line 62: Paragraph is unclear at first reading. Better to rephrase.
- Line 78: missing "with high probability" at the end of sentence?
- Paragraph starting at line 79 is highly unclear. E.g. "compress the sparsity" makes no sense. "bit complexity" is unclear.
- Definition 1: Unclear to what "with probability 1-\delta" refers to. Is it to property P or the empirical error bound. Missing comma? What is the property P you actually use in your proofs?
- Thm. 2 has many typos. In addition, missing iid assumption. Is there no assumption on the loss function l? More importantly, in the refereed papers, only one direction of the bound is proved, as opposed to the bidirectional (i.e. having absolute value on the lhs of the bound) you give here.
- Definition 2 has many typos.
- Line 74: "Let us say" -> Suppose?; Also eps -> (eps,m)?
- Line 187: "improve the dependence on sqrt(m)". Please add reference.
- The sentence beginning in line 292 is highly unclear.
- Many typos all over.
- The bibliography misses many entries (year, journal, etc.).
References:
[KSW] - Kontorovich, Sabato and Weiss. Nearest-Neighbor Sample Compression: Efficiency, Consistency, Infinite Dimensions. 2017.
[Zhang] - Tong Zhang. Effective dimension and generalization of kernel learning. 2013.