5 citations · 21 across the 19 of their papers we have counts for
21 papers
Adaptive Kernel Ridge Regression with Linear Structure: Sharp Oracle Inequalities and Minimax Optimality
Xin Bing, Chao Wang
Kernel ridge regression (KRR) is a widely used nonparametric method due to its strong theoretical guarantees and computational convenience. However, standard KRR does not distingui…
Convergence and Optimality of the EM Algorithm Under Multi-Component Gaussian Mixture Models
Xin Bing, Dehan Kong, Bingqing Li
Gaussian mixture models (GMMs) are fundamental statistical tools for modeling heterogeneous data. Due to the nonconcavity of the likelihood function, the Expectation-Maximization (…
Federated Independent Component Analysis via Spectral Alignment and Robust Aggregation
Dian Jin, Xin Bing, Yuqian Zhang
This paper studies robust estimation for Independent Component Analysis (ICA) in the federated learning setting, where data are locally distributed across clients and may exhibit s…
High-Dimensional Invariant Tests of Multivariate Normality Based on Radial Concentration
Xin Bing, Derek Latremouille
While the problem of testing multivariate normality has received considerable attention in the classical low-dimensional setting where the sample size is much larger than the f…
The EM-algorithm and the Method of Moments in Softmax Mixture Models
Xin Bing, Florentina Bunea, Jonathan Niles-Weed +1
Softmax Mixture Models (SMMs) are discrete -component mixture models for the probabilities of selecting one of candidate feature vectors in h…
A New Regression Lens on Multi-Class Classification
Xin Bing, Bingqing Li, Marten Wegkamp
Linear Discriminant Analysis (LDA) is a fundamental method for classification. Its simple linear structure facilitates interpretation, and it is naturally suited to multi-class set…
Optimal vintage factor analysis with deflation varimax
Xin Bing, Xin He, Dian Jin +1
Vintage factor analysis is one important type of factor analysis that aims to first find a low-dimensional representation of the original data, and then to seek a rotation such tha…
Interpolating Discriminant Functions in High-Dimensional Gaussian Latent Mixtures
Xin Bing, Marten Wegkamp
This paper considers binary classification of high-dimensional features under a postulated model with a low-dimensional latent Gaussian mixture structure and non-vanishing noise. A…
Optimal Discriminant Analysis in High-Dimensional Latent Factor Models
Xin Bing, Marten Wegkamp
In high-dimensional classification problems, a commonly used approach is to first project the high-dimensional features into a lower dimensional space, and base the classification…
Estimation and inference for the Wasserstein distance between mixing measures in topic models
Xin Bing, Florentina Bunea, Jonathan Niles-Weed
The Wasserstein distance between mixing measures has come to occupy a central place in the statistical analysis of mixture models. This work proposes a new canonical interpretation…
Inference in High-dimensional Multivariate Response Regression with Hidden Variables
Xin Bing, Wei Cheng, Huijie Feng +1
This paper studies the inference of the regression coefficient matrix under multivariate response linear regressions in the presence of hidden variables. A novel procedure for cons…
Likelihood estimation of sparse topic distributions in topic models and its applications to Wasserstein document distance calculations
Xin Bing, Florentina Bunea, Seth Strimas-Mackey +1
This paper studies the estimation of high-dimensional, discrete, possibly sparse, mixture models in topic models. The data consists of observed multinomial counts of words acro…
Unique sparse decomposition of low rank matrices
Dian Jin, Xin Bing, Yuqian Zhang
The problem of finding the unique low dimensional decomposition of a given matrix has been a fundamental and recurrent problem in many areas. In this paper, we study the problem of…
Detecting approximate replicate components of a high-dimensional random vector with latent structure
Xin Bing, Florentina Bunea, Marten Wegkamp
High-dimensional feature vectors are likely to contain sets of measurements that are approximate replicates of one another. In complex applications, or automated data collection, t…
Prediction in latent factor regression: Adaptive PCR and beyond
Xin Bing, Florentina Bunea, Seth Strimas-Mackey +1
This work is devoted to the finite sample prediction risk analysis of a class of linear predictors of a response from a high-dimensional random vector $X\in \math…
Adaptive Estimation in Multivariate Response Regression with Hidden Variables
Xin Bing, Yang Ning, Yaosheng Xu
This paper studies the estimation of the coefficient matrix $\Ttheta$ in multivariate regression with hidden variables, $Y = (\Ttheta)^TX + (B^*)^TZ + E$, where is a -dimens…
Optimal estimation of sparse topic models
Xin Bing, Florentina Bunea, Marten Wegkamp
Topic models have become popular tools for dimension reduction and exploratory analysis of text data which consists in observed frequencies of a vocabulary of words in docu…
Inference in latent factor regression with clusterable features
Xin Bing, Florentina Bunea, Marten Wegkamp
Regression models, in which the observed features and the response depend, jointly, on a lower dimensional, unobserved, latent vector , with $K<…
A fast algorithm with minimax optimal guarantees for topic models with an unknown number of topics
Xin Bing, Florentina Bunea, Marten Wegkamp
We propose a new method of estimation in topic models, that is not a variation on the existing simplex finding algorithms, and that estimates the number of topics K from the observ…
Adaptive Estimation in Structured Factor Models with Applications to Overlapping Clustering
Xin Bing, Florentina Bunea, Yang Ning +1
This work introduces a novel estimation method, called LOVE, of the entries and structure of a loading matrix A in a sparse latent factor model X = AZ + E, for an observable random…
Adaptive estimation of the rank of the coefficient matrix in high dimensional multivariate response regression models
Xin Bing, Marten Wegkamp
We consider the multivariate response regression problem with a regression coefficient matrix of low, unknown rank. In this setting, we analyze a new criterion for selecting the op…