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- Publications
- Influence

Orthogonal Matching Pursuit for Sparse Signal Recovery With Noise

We consider the orthogonal matching pursuit (OMP) algorithm for the recovery of a high-dimensional sparse signal based on a small number of noisy linear measurements. OMP is an iterative greedy… Expand

Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming

- A. Belloni, V. Chernozhukov, L. Wang
- Mathematics
- 13 June 2011

We propose a pivotal method for estimating high-dimensional sparse linear regression models, where the overall number of regressors p is large, possibly much larger than n, but only s regressors are… Expand

Square-Root Lasso: Pivotal Recovery of Sparse Signals via Conic Programming

- A. Belloni, V. Chernozhukov, L. Wang
- Mathematics
- 28 September 2010

We propose a pivotal method for estimating high-dimensional sparse linear regression models, where the overall number of regressors p is large, possibly much larger than n, but only s regressors are… Expand

New Bounds for Restricted Isometry Constants

- T. T. Cai, L. Wang, Guangwu Xu
- Computer Science, Mathematics
- IEEE Transactions on Information Theory
- 8 November 2009

This paper discusses new bounds for restricted isometry constants in compressed sensing. Let Φ be an n × p real matrix and A; be a positive integer with k ≤ n. One of the main results of this paper… Expand

Stable Recovery of Sparse Signals and an Oracle Inequality

- T. T. Cai, L. Wang, Guangwu Xu
- Mathematics, Computer Science
- IEEE Transactions on Information Theory
- 1 July 2010

This article considers sparse signal recovery in the presence of noise. A mutual incoherence condition which was previously used for exact recovery in the noiseless case is shown to be sufficient for… Expand

Shifting Inequality and Recovery of Sparse Signals

- T. T. Cai, L. Wang, Guangwu Xu
- Mathematics, Computer Science
- IEEE Transactions on Signal Processing
- 1 February 2010

In this paper, we present a concise and coherent analysis of the constrained ¿1 minimization method for stable recovering of high-dimensional sparse signals both in the noiseless case and noisy case.… Expand

The L1L1 penalized LAD estimator for high dimensional linear regression

- L. Wang
- Computer Science, Mathematics
- J. Multivar. Anal.
- 1 September 2013

In this paper, the high-dimensional sparse linear regression model is considered, where the overall number of variables is larger than the number of observations. We investigate the L"1 penalized… Expand

Pivotal estimation via square-root Lasso in nonparametric regression

- A. Belloni, V. Chernozhukov, L. Wang
- Mathematics
- 7 May 2011

We propose a self-tuning $\sqrt{\mathrm {Lasso}}$ method that simultaneously resolves three important practical problems in high-dimensional regression analysis, namely it handles the unknown scale,… Expand

Orthogonal Matching Pursuit for Sparse Signal Recovery

We consider the orthogonal matching pursuit (OMP) algorithm for the recovery of a high-dimensional sparse signal based on a small number of noisy linear measurements. OMP is an iterative greedy… Expand

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TIGER: A Tuning-Insensitive Approach for Optimally Estimating Gaussian Graphical Models

We propose a new procedure for estimating high dimensional Gaussian graphical models. Our approach is asymptotically tuning-free and non-asymptotically tuning-insensitive: it requires very few… Expand