36 citations · 61 across the 6 of their papers we have counts for
6 papers
Fixed-Threshold One-Bit Toeplitz Covariance Estimation under Sparse-Ruler Sampling
Zhiyong Cheng, Shengyao Chen
We estimate the Toeplitz covariance matrix of a centered Gaussian distribution from data that are both coarsely quantized and sparsely sampled. Only the coordinates of a sparse rul…
Sparse Linear Isotonic Models
Sheng Chen, Arindam Banerjee
In machine learning and data mining, linear models have been widely used to model the response as parametric linear functions of the predictors. To relax such stringent assumptions…
Alternating Estimation for Structured High-Dimensional Multi-Response Models
Sheng Chen, Arindam Banerjee
We consider learning high-dimensional multi-response linear models with structured parameters. By exploiting the noise correlations among responses, we propose an alternating estim…
Structured Matrix Recovery via the Generalized Dantzig Selector
Sheng Chen, Arindam Banerjee
In recent years, structured matrix recovery problems have gained considerable attention for its real world applications, such as recommender systems and computer vision. Much of th…
Estimation with Norm Regularization
Arindam Banerjee, Sheng Chen, Farideh Fazayeli +1
Analysis of non-asymptotic estimation error and structured statistical recovery based on norm regularized regression, such as Lasso, needs to consider four aspects: the norm, the l…
Generalized Dantzig Selector: Application to the k-support norm
Soumyadeep Chatterjee, Sheng Chen, Arindam Banerjee
We propose a Generalized Dantzig Selector (GDS) for linear models, in which any norm encoding the parameter structure can be leveraged for estimation. We investigate both computati…