307 citations · 907 across the 65 of their papers we have counts for
7 papers · 1 filter
Self-regularizing Property of Nonparametric Maximum Likelihood Estimator in Mixture Models
Yury Polyanskiy, Yihong Wu
Introduced by Kiefer and Wolfowitz \cite{KW56}, the nonparametric maximum likelihood estimator (NPMLE) is a widely used methodology for learning mixture odels and empirical Bayes e…
Learning Mixtures of Permutations: Groups of Pairwise Comparisons and Combinatorial Method of Moments
Cheng Mao, Yihong Wu
In applications such as rank aggregation, mixture models for permutations are frequently used when the population exhibits heterogeneity. In this work, we study the widely used Mal…
Note on approximating the Laplace transform of a Gaussian on a complex disk
Yury Polyanskiy, Yihong Wu
In this short note we study how well a Gaussian distribution can be approximated by distributions supported on . Perhaps, the natural conjecture is that for large the a…
Testing correlation of unlabeled random graphs
Yihong Wu, Jiaming Xu, Sophie H. Yu
We study the problem of detecting the edge correlation between two random graphs with unlabeled nodes. This is formalized as a hypothesis testing problem, where under the null…
Extrapolating the profile of a finite population
Soham Jana, Yury Polyanskiy, Yihong Wu
We study a prototypical problem in empirical Bayes. Namely, consider a population consisting of individuals each belonging to one of types (some types can be empty). Withou…
Likelihood landscape and maximum likelihood estimation for the discrete orbit recovery model
Zhou Fan, Yi Sun, Tianhao Wang +1
We study the non-convex optimization landscape for maximum likelihood estimation in the discrete orbit recovery model with Gaussian noise. This model is motivated by applications i…
Optimal estimation of high-dimensional location Gaussian mixtures
Natalie Doss, Yihong Wu, Pengkun Yang +1
This paper studies the optimal rate of estimation in a finite Gaussian location mixture model in high dimensions without separation conditions. We assume that the number of compone…