Search Results for author:
Found 9 papers, 0 papers with code
Debiasing and a local analysis for population clustering using semidefinite programming
In particular, we analyze computational efficient algorithms proposed by the same author, to partition data into two groups approximately according to their population of origin given a small sample.
Precision Matrix Estimation with Noisy and Missing Data
Estimating conditional dependence graphs and precision matrices are some of the most common problems in modern statistics and machine learning.
Errors-in-variables models with dependent measurements
Under sparsity and restrictive eigenvalue type of conditions, we show that one is able to recover a sparse vector $\beta^* \in \mathbb{R}^m$ from the model given a single observation matrix $X$ and the response vector $y$.
Joint mean and covariance estimation with unreplicated matrix-variate data
It has been proposed that complex populations, such as those that arise in genomics studies, may exhibit dependencies among observations as well as among variables.
High dimensional errors-in-variables models with dependent measurements
Suppose that we observe $y \in \mathbb{R}^f$ and $X \in \mathbb{R}^{f \times m}$ in the following errors-in-variables model: \begin{eqnarray*} y & = & X_0 \beta^* + \epsilon \\ X & = & X_0 + W \end{eqnarray*} where $X_0$ is a $f \times m$ design matrix with independent subgaussian row vectors, $\epsilon \in \mathbb{R}^f$ is a noise vector and $W$ is a mean zero $f \times m$ random noise matrix with independent subgaussian column vectors, independent of $X_0$ and $\epsilon$.
Gemini: Graph estimation with matrix variate normal instances
Under sparsity conditions, we show that one is able to recover the graphs and covariance matrices with a single random matrix from the matrix variate normal distribution.
Convergence Properties of Kronecker Graphical Lasso Algorithms
The KGlasso algorithm generalizes Glasso, introduced by Yuan and Lin ["Model selection and estimation in the Gaussian graphical model," Biometrika, vol.
Thresholding Procedures for High Dimensional Variable Selection and Statistical Estimation
Given $n$ noisy samples with $p$ dimensions, where $n \ll p$, we show that the multi-stage thresholding procedures can accurately estimate a sparse vector $\beta \in \R^p$ in a linear model, under the restricted eigenvalue conditions (Bickel-Ritov-Tsybakov 09).
Compressed Regression
Recent research has studied the role of sparsity in high dimensional regression and signal reconstruction, establishing theoretical limits for recovering sparse models from sparse data.
