Paper:
A New Theory for Matrix Completion
Prevalent matrix completion theories reply on an assumption that the locations of the missing data are distributed uniformly and randomly (i.e., uniform sampling). Nevertheless, the reason for observations being missing often depends on the unseen observations themselves, and thus the missing data in practice usually occurs in a nonuniform and deterministic fashion rather than randomly. To break through the limits of random sampling, this paper introduces a new hypothesis called isomeric condition, which is provably weaker than the assumption of uniform sampling and arguably holds even when the missing data is placed irregularly. Equipped with this new tool, we prove a series of theorems for missing data recovery and matrix completion. In particular, we prove that the exact solutions that identify the target matrix are included as critical points by the commonly used nonconvex programs. Unlike the existing theories for nonconvex matrix completion, which are built upon the same condition as convex programs, our theory shows that nonconvex programs have the potential to work with a much weaker condition. Comparing to the existing studies on nonuniform sampling, our setup is more general.

Review:
The authors study matrix completion from a few entries.  They propose a new
proprty of (matrix,sampling pattern) which they term "Isomery". They show that
the well known (incoherence,low rank,random sampling) assumption implies this
condition. The authors then consider a nonconvex bilinear program for matrix
completion. Under the Isomery assumption, they prove that the exact solution is
a critical point of this program (with no assumptions on rank, it seems).  Some
empirical evidence are presented, whereby the nonconvex program is superior to
the convex Nuclear Norm minimization method for a certain pattern of nonuniform
sampling.
The paper is relatively well written (see comments on English below). The
results are novel as far as I can tell, and interesting. This work touches on
fundamental aspects of matrix recovery, and can certainly lead to new research.
There are a few major comments I'd be happy to see addressed. That said, I vote
 accept. 
Major comments:
1. The authors say nothing about the natural questions: Can the Isometry
condition be verified in practice? Is it a reasonable assumption to make?
2. The authors discuss in the introduction reasons why random sampling may not
be a reasonable assumption, with some real data examples. It would be nice if,
for a real full data matrix, and a sampling pattern of the kind shown, the
    authors can verify the Isomry assumption. 
3. The matrix recovery problem, as well as all the algorithms discussed in this
paper, are invariant under row and col permutations. The authors discuss
sampling patterns which are "concentrated around the main diagonal" - this
condition is not invariant under permutations. Only invariant conditions make
sense.
4. The main contributions claimed (l.79) are not backed up by the paper itself. 
Specifically, 
    - it's unclear what is meant by "our theories are more flexible and
powerful"
    - the second bullet point in the main contribution list (l.83) referes
 to "nonconvex programs" in general
    - The third bullet point (l.87) is not actuallty proved, and should not be
      advertised as a main contribution.  The authors 
  proved Theorem 3.1, but its connection to matrix completion is only vageuly described
  in the paragraph above Theorem 3.1.
minor comments: 
- English throughout
- Theorem 3.2: formally define "with high probability"
- The word "theory" and "theories" in the title and throughout: I'm a non-native English speaker, yet I don't see
why you use the word. 
- l.56: "incoherent assumption" -> "incoherence assumption"
- l.75 "by the commonly used" -> "of the commonly used"
- l.79 "hypothesis" -> condition
- l.92, l.129 "proof processes" -> "proofs"
- l.188 "where it denotes" -> "where we denote"
- l.208 "that" -??
- Eq. (8) and Eq. (9) - maybe missing =0 on the right hand side?
- Figure 2: label vertical axis
- what do you mean by "proof of theory" (eg line 92)?