Breaking the $1/\sqrt{n}$ Barrier: Faster Rates for Permutation-based Models in Polynomial Time

Abstract: Many applications, including rank aggregation and crowd-labeling, can be modeled in terms of a bivariate isotonic matrix with unknown permutations acting on its rows and columns. We consider the problem of estimating such a matrix based on noisy observations of a subset of its entries, and design and analyze a polynomial-time algorithm that improves upon the state of the art. In particular, our results imply that any such $n \times n$ matrix can be estimated efficiently in the normalized Frobenius norm at rate $\widetilde{\mathcal O}(n<sup>{-3/4})$, thus narrowing the gap between $\widetilde{\mathcal O}(n<sup>{-1})$ and $\widetilde{\mathcal O}(n<sup>{-1/2})$, which were hitherto the rates of the most statistically and computationally efficient methods, respectively.