NeurIPS 2019
Sun Dec 8th through Sat the 14th, 2019 at Vancouver Convention Center
Paper ID:6733
Title:A General Framework for Symmetric Property Estimation

Reviewer 1

The contributions in this paper are quite interesting and possibly significant. The paper fits in an ongoing series of developments for the studied problem, and gives a nice contribution. The main issue with the paper is that it feels extremely dry and it is hard to follow at times. Surely in part this limited readability is due to the relatively high technical level and the page limitation, but more intuition could be given to the reader to help them follow the ongoing reasoning, and to ensure that the paper is at least in part self-contained (for example, some intuition on the proofs of Thm 3.1 and 3.2, and Lemma 4.1 would be helpful).

Reviewer 2

The paper studies two open questions in the PML framework - approximately calculating the PML and showing the optimally of PML based estimation for a larger accuracy range - epsilon. For the problem of support estimation, it is shown that an approximate PML is sample optimal in the entire range of epsilon. For the problem of entropy and distance to uniformity, the authors devise a new generalization of PML, called the Pseudo PML. They do so by defining the S-pseudo profile, using the S-PML estimate for the 'light' elements and the empirical estimate for the 'heavy' elements. Using this, they derive sample optimal rates for these problems in the entire range of epsilon. The results are interesting and resolves some intriguing open questions in [ADOS16]. Pseudo PML is a natural extension of PML and I find the general framework of Algorithm 1 elegant and right in principle. I'm overall satisfied with the writing of the paper, and the authors provide a good sketch of their proofs. I'm also satisfied with the citations of relevant work. I think the paper is interesting and vote to accept the paper. Post Rebuttal: While the authors have addressed some of my concerns, I'm maintaining my score. The authors have to adequately address how PseudoPML is efficiency computable. At least some experimental evaluations that suggest that it is possible to do so.

Reviewer 3

I would like to recommend the paper for acceptance but hesitate due to the following reasons. I think that the paper may need further improvement. 1. Let me begin with a few suggestions/comments on the literature review part (Section 1.1). 1) line 70, "... the empirical distribution is sample competitive", should this be "sample optimal"? 2) line 74, it seems that the sample complexity upper bound obtained [VV11b] is (N/\epsilon^2\log N ) instead of (N/\epsilon\log N ). 3) line 76, maybe it is better to define "u" as "the uniform distribution over D" before using it. 4) line 81, it seems that according to [WY15], one should use [1/N, 1] instead of [1/k, 1]. 5) line 90, [VV11b] does not seem to mention "distance to uniformity" or "support coverage". 6) line 93, the estimator in [HJW18] does not seem to achieve "the optimal sample complexity in all error regimes". According to the last paragraph on page 12 of [HJW18]. The estimator has a sub-optimal standard deviation in all cases and works for \epsilon>n^{-0.33}. 7) line 95, in a recent work of some of the [YOSW18]'s authors, the "n to n\sqrt{\log n}" "data amplification" has been strengthened to "n to n\log n". The new paper is available at "", it would be good to also mention this stronger result. 2. The pseudo-PML is novel as the previous PML method always utilizes the full profile, which may not be the best choice for certain problems. The submission also claimed that the method is "efficient" (both in the title and the main body), and "more practical" (abstract, line 9), so I expect to see an efficient algorithm and some experimental results. However, I was not able to find such contents in the current version. 3. I am slightly concerned about the proof of the support-estimation result (Theorem 3.1), which appears in the supplemental material. The proof appears between line 342 and 358. The inequality above line 357 states that given a sample size n>2k/(\log k), the probability that S(p)=Distinct(\phi) is >= 1-exp(n/k). It seems that 1-exp(n/k) is a negative number for any positive integer n, and it is not meaningful to show that a probability is always >= a negative quantity. It is possible that the expression missed a negative sign inside the exponential function. However, even if that is the case, for some n>2k/(\log k), say n=3k/(\log k), the quantity 1-exp(-n/k) is roughly 3/(\log k) for large k, which can be arbitrarily close to 0 as k increases. Hence, we do not have a high-probability guarantee here, e.g., "with probability at least 2/3". 4. The submission seems to suggest that the proposed method is universal and property-independent, e.g., line 101, 188. The detailed construction of these estimators appears in Section 5: Proof of theorem 3.9 and Proof of theorem 3.10. According to the construction, for the properties considered in the paper, namely, entropy and distance to uniformity, one must first choose a specific set F of frequencies according to that property, and then compute the pseudo-PML. Subjectively, I think it may be more accurate to say that this method "weakly depends" on the property. 5. Some terms and notations appeared in the paper before being defined: 1) line 37: "separable" 2) line 72 to 78: "N" 3) line 160 to 161: "F" ===========After rebuttal========== I would like to thank the authors for clarifying the points above. The rebuttal stated that "the key idea of pseudoPML is to use an algorithm ([CSS19]) for PML as a subroutine in a black-box way". Yet I don't see how to use that algorithm to compute a pseudoPML. The paper's title emphasizes that the method is "efficient", I hope that the authors can present solid proofs for this claim. I think that simply "invoke the [CSS19] efficient approximate PML algorithm on frequencies up to O(log N)" will not work here. For example, we can choose two samples, one is of size N and the other is of size N^2, such that the first one only contains distinct symbols, and the second one coincides with the first in its first N entries, and has only one (distinct) symbol in the remaining N^2-N entries. In both cases, invoking "the [CSS19] efficient approximate PML algorithm on frequencies up to O(log N)" will yield the same distribution estimate. Yet it is very likely that the two underlying distributions have quite different entropy values. It is possible that I am missing something, but I do not see it from the paper/rebuttal. For this reason, I would like to keep my original overall score.