435 citations · 1.7k across the 22 of their papers we have counts for
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Distributed Machine Learning with Sparse Heterogeneous Data
Dominic Richards, Sahand N. Negahban, Patrick Rebeschini
Motivated by distributed machine learning settings such as Federated Learning, we consider the problem of fitting a statistical model across a distributed collection of heterogeneo…
A Unified Framework for High-Dimensional Analysis of M-Estimators with Decomposable Regularizers
Sahand N. Negahban, Pradeep Ravikumar, Martin J. Wainwright +1
High-dimensional statistical inference deals with models in which the the number of parameters p is comparable to or larger than the sample size n. Since it is usually impossible t…
Estimation of (near) low-rank matrices with noise and high-dimensional scaling
Sahand Negahban, Martin J. Wainwright
High-dimensional inference refers to problems of statistical estimation in which the ambient dimension of the data may be comparable to or possibly even larger than the sample size…
Simultaneous support recovery in high dimensions: Benefits and perils of block -regularization
S. Negahban, M. J. Wainwright
Consider the use of -regularized regression for joint estimation of a $\pdim \times \numreg$ matrix of regression coefficients. We analyze the high-dimensio…