435 citations · 1.7k across the 22 of their papers we have counts for
23 papers
Tree-Projected Gradient Descent for Estimating Gradient-Sparse Parameters on Graphs
Sheng Xu, Zhou Fan, Sahand Negahban
We study estimation of a gradient-sparse parameter vector , having strong gradient-sparsity on an underlying g…
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…
Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback
Chicheng Zhang, Alekh Agarwal, Hal Daumé +2
We investigate the feasibility of learning from a mix of both fully-labeled supervised data and contextual bandit data. We specifically consider settings in which the underlying le…
Alternating Linear Bandits for Online Matrix-Factorization Recommendation
Hamid Dadkhahi, Sahand Negahban
We consider the problem of online collaborative filtering in the online setting, where items are recommended to the users over time. At each time step, the user (selected by the en…
Feature Selection using Stochastic Gates
Yutaro Yamada, Ofir Lindenbaum, Sahand Negahban +1
Feature selection problems have been extensively studied for linear estimation, for instance, Lasso, but less emphasis has been placed on feature selection for non-linear functions…
Minimax Estimation of Bandable Precision Matrices
Addison Hu, Sahand Negahban
The inverse covariance matrix provides considerable insight for understanding statistical models in the multivariate setting. In particular, when the distribution over variables is…