307 citations · 907 across the 65 of their papers we have counts for
67 papers
Entropy of Bernoulli Measures Conditioned on Affine Subspaces and a Problem of Ancheta--Massey
Yihong Wu
A textbook result in information theory is that linear encoders achieve the entropy for lossless compression of Bernoulli source with parameter . For lossy compression, however,…
Empirical Bayes via ERM and Rademacher complexities: the Poisson model
Soham Jana, Yury Polyanskiy, Anzo Teh +1
We consider the problem of empirical Bayes estimation for (multivariate) Poisson means. Existing solutions that have been shown theoretically optimal for minimizing the regret (exc…
Entropic characterization of optimal rates for learning Gaussian mixtures
Zeyu Jia, Yury Polyanskiy, Yihong Wu
We consider the question of estimating multi-dimensional Gaussian mixtures (GM) with compactly supported or subgaussian mixing distributions. Minimax estimation rate for this class…
Large-scale Multiple Testing: Fundamental Limits of False Discovery Rate Control and Compound Oracle
Yutong Nie, Yihong Wu
The false discovery rate (FDR) and the false non-discovery rate (FNR), defined as the expected false discovery proportion (FDP) and the false non-discovery proportion (FNP), are th…
Random linear estimation with rotationally-invariant designs: Asymptotics at high temperature
Yufan Li, Zhou Fan, Subhabrata Sen +1
We study estimation in the linear model , in a Bayesian setting where has an entrywise i.i.d. prior and the design is rotationally-invariant in law. In…
Empirical Bayes estimation: When does -modeling beat -modeling in theory (and in practice)?
Yandi Shen, Yihong Wu
Empirical Bayes (EB) is a popular framework for large-scale inference that aims to find data-driven estimators to compete with the Bayesian oracle that knows the true prior. Two pr…
Random graph matching at Otter's threshold via counting chandeliers
Cheng Mao, Yihong Wu, Jiaming Xu +1
We propose an efficient algorithm for graph matching based on similarity scores constructed from counting a certain family of weighted trees rooted at each vertex. For two Erdős-Ré…
Optimal empirical Bayes estimation for the Poisson model via minimum-distance methods
Soham Jana, Yury Polyanskiy, Yihong Wu
The Robbins estimator is the most iconic and widely used procedure in the empirical Bayes literature for the Poisson model. On one hand, this method has been recently shown to be m…
Random Graph Matching in Geometric Models: the Case of Complete Graphs
Haoyu Wang, Yihong Wu, Jiaming Xu +1
This paper studies the problem of matching two complete graphs with edge weights correlated through latent geometries, extending a recent line of research on random graph matching…
Testing network correlation efficiently via counting trees
Cheng Mao, Yihong Wu, Jiaming Xu +1
We propose a new procedure for testing whether two networks are edge-correlated through some latent vertex correspondence. The test statistic is based on counting the co-occurrence…
Sharp regret bounds for empirical Bayes and compound decision problems
Yury Polyanskiy, Yihong Wu
We consider the classical problems of estimating the mean of an -dimensional normally (with identity covariance matrix) or Poisson distributed vector under the squared loss. In…
Optimal prediction of Markov chains with and without spectral gap
Yanjun Han, Soham Jana, Yihong Wu
We study the following learning problem with dependent data: Observing a trajectory of length from a stationary Markov chain with states, the goal is to predict the next st…
The replica-symmetric free energy for Ising spin glasses with orthogonally invariant couplings
Zhou Fan, Yihong Wu
We study a variant of the Sherrington-Kirkpatrick (S-K) spin glass model with external field, where the random symmetric couplings matrix does not consist of i.i.d. entries but is…
Polynomial methods in statistical inference: theory and practice
Yihong Wu, Pengkun Yang
This survey provides an exposition of a suite of techniques based on the theory of polynomials, collectively referred to as polynomial methods, which have recently been applied to…
The planted matching problem: Sharp threshold and infinite-order phase transition
Jian Ding, Yihong Wu, Jiaming Xu +1
We study the problem of reconstructing a perfect matching hidden in a randomly weighted bipartite graph. The edge set includes every node pair in and each o…
Settling the Sharp Reconstruction Thresholds of Random Graph Matching
Yihong Wu, Jiaming Xu, Sophie H. Yu
This paper studies the problem of recovering the hidden vertex correspondence between two edge-correlated random graphs. We focus on the Gaussian model where the two graphs are com…
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…
Consistent recovery threshold of hidden nearest neighbor graphs
Jian Ding, Yihong Wu, Jiaming Xu +1
Motivated by applications such as discovering strong ties in social networks and assembling genome subsequences in biology, we study the problem of recovering a hidden -nearest…