16 citations · 41 across the 5 of their papers we have counts for
8 papers
Extreme Compressive Sampling for Covariance Estimation
Martin Azizyan, Akshay Krishnamurthy, Aarti Singh
This paper studies the problem of estimating the covariance of a collection of vectors using only highly compressed measurements of each vector. An estimator based on back-projecti…
Risk Bounds For Mode Clustering
Martin Azizyan, Yen-Chi Chen, Aarti Singh +1
Density mode clustering is a nonparametric clustering method. The clusters are the basins of attraction of the modes of a density estimator. We study the risk of mode-based cluster…
Feature Selection For High-Dimensional Clustering
Larry Wasserman, Martin Azizyan, Aarti Singh
We present a nonparametric method for selecting informative features in high-dimensional clustering problems. We start with a screening step that uses a test for multimodality. The…
Efficient Sparse Clustering of High-Dimensional Non-spherical Gaussian Mixtures
Martin Azizyan, Aarti Singh, Larry Wasserman
We consider the problem of clustering data points in high dimensions, i.e. when the number of data points may be much smaller than the number of dimensions. Specifically, we consid…
Subspace Learning from Extremely Compressed Measurements
Akshay Krishnamurthy, Martin Azizyan, Aarti Singh
We consider learning the principal subspace of a large set of vectors from an extremely small number of compressive measurements of each vector. Our theoretical results show that e…
Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation
Martin Azizyan, Aarti Singh, Larry Wasserman
While several papers have investigated computationally and statistically efficient methods for learning Gaussian mixtures, precise minimax bounds for their statistical performance…
Density-sensitive semisupervised inference
Martin Azizyan, Aarti Singh, Larry Wasserman
Semisupervised methods are techniques for using labeled data together with unlabeled data to make predictions. These methods invok…
Adaptive Semisupervised Inference
Martin Azizyan, Aarti Singh, Larry Wasserman
Semisupervised methods inevitably invoke some assumption that links the marginal distribution of the features to the regression function of the label. Most commonly, the cluster or…