# The Bethe and Sinkhorn Permanents of Low Rank Matrices and Implications for Profile Maximum Likelihood

arXiv: https://arxiv.org/abs/2004.02425 · HTML: https://mlsanity.com/paper/2004.02425/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2020.

## Built on by (most cited first, 1 of 1)

- [On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood](https://mlsanity.com/paper/2210.06728/) (2022)

## Abstract (arXiv)

In this paper we consider the problem of computing the likelihood of the profile of a discrete distribution, i.e., the probability of observing the multiset of element frequencies, and computing a profile maximum likelihood (PML) distribution, i.e., a distribution with the maximum profile likelihood. For each problem we provide polynomial time algorithms that given n i.i.d. samples from a discrete distribution, achieve an approximation factor of expleft(-O(√n log n) right), improving upon the previous best-known bound achievable in polynomial time of exp(-O(n2/3 log n)) (Charikar, Shiragur and Sidford, 2019). Through the work of Acharya, Das, Orlitsky and Suresh (2016), this implies a polynomial time universal estimator for symmetric properties of discrete distributions in a broader range of error parameter. We achieve these results by providing new bounds on the quality of approximation of the Bethe and Sinkhorn permanents (Vontobel, 2012 and 2014). We show that each of these are exp(O(k log(N/k))) approximations to the permanent of N × N matrices with non-negative rank at most k, improving upon the previous known bounds of exp(O(N)). To obtain our results on PML, we exploit the fact that the PML objective is proportional to the permanent of a certain Vandermonde matrix with √n distinct columns, i.e. with non-negative rank at most √n. As a by-product of our work we establish a surprising connection between the convex relaxation in prior work (CSS19) and the well-studied Bethe and Sinkhorn approximations.