Search Results for author: Martin Azizyan

Found 7 papers, 0 papers with code

Extreme Compressive Sampling for Covariance Estimation

no code implementations2 Jun 2015 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.

Efficient Sparse Clustering of High-Dimensional Non-spherical Gaussian Mixtures

no code implementations9 Jun 2014 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.

Subspace Learning from Extremely Compressed Measurements

no code implementations3 Apr 2014 Akshay Krishnamurthy, Martin Azizyan, Aarti Singh

Our theoretical results show that even a constant number of measurements per column suffices to approximate the principal subspace to arbitrary precision, provided that the number of vectors is large.

Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation

no code implementations NeurIPS 2013 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 as well as fundamental limits in high-dimensional settings are not well-understood.

Feature Selection

Density-sensitive semisupervised inference

no code implementations7 Apr 2012 Martin Azizyan, Aarti Singh, Larry Wasserman

Semisupervised methods are techniques for using labeled data $(X_1, Y_1),\ldots,(X_n, Y_n)$ together with unlabeled data $X_{n+1},\ldots, X_N$ to make predictions.

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