246 citations · 927 across the 27 of their papers we have counts for
28 papers
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…
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…
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…
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…